This document proposes a new anti-forensic technique to hide evidence of compression history and manipulations in medical bitmap images. Existing methods for identifying compression history include JPEG detection and quantization table estimation, which analyze image transform coefficients for variations and blocking artifacts from compression. The proposed method indicates that properly adding noise to an image's transform coefficients can eliminate quantization artifacts and compression indicators, making the image appear as if it had never been compressed. This anti-forensic technique aims to cover up a medical image's processing history and enable undetectable image tampering.
Medical Image Processing in Nuclear Medicine and Bone ArthroplastyIOSR Journals
This document discusses medical image processing in nuclear medicine and bone arthroplasty. It provides background on nuclear medicine imaging techniques like planar imaging, SPECT, PET and hybrid SPECT/CT and PET/CT systems. It then discusses how MATLAB can be used for medical image processing tasks in nuclear medicine like organ contouring, interpolation, filtering, segmentation, background removal, registration and volume quantification. Specific examples of nuclear medicine examinations that can be analyzed using MATLAB algorithms are also mentioned.
Lec1: Medical Image Computing - Introduction Ulaş Bağcı
2017 Spring, UCF Medical Image Computing Course
Basics of Radiological Image Modalities and their clinical use (MRI, PET, CT, fMRI, DTI, ...)
• Introduction to Medical Image Computing and Toolkits
• Image Filtering, Enhancement, Noise Reduction, and
Signal Processing
• MedicalImageRegistration
• MedicalImageSegmentation
• MedicalImageVisualization
• Machine Learning in Medical Imaging
• Shape Modeling/Analysis of Medical Images
Deep Learning in Radiology
Brain Tumor Detection using MRI ImagesYogeshIJTSRD
Brain tumor segmentation is a very important task in medical image processing. Early diagnosis of brain tumors plays a crucial role in improving treatment possibilities and increases the survival rate of the patients. For the study of tumor detection and segmentation, MRI Images are very useful in recent years. One of the foremost crucial tasks in any brain tumor detection system is that the detachment of abnormal tissues from normal brain tissues. Because of MRI Images, we will detect the brain tumor. Detection of unusual growth of tissues and blocks of blood within the system is seen in an MRI Imaging. Brain tumor detection using MRI images may be a challenging task due to the brains complex structure.In this paper, we propose an image segmentation method to detect tumors from MRI images using an interface of GUI in MATLAB. The method of distinguishing brain tumors through MRI images is often sorted into four sections of image processing as pre processing, feature extraction, image segmentation, and image classification. During this paper, weve used various algorithms for the partial fulfillment of the necessities to hit the simplest results that may help us to detect brain tumors within the early stage. Deepa Dangwal | Aditya Nautiyal | Dakshita Adhikari | Kapil Joshi "Brain Tumor Detection using MRI Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Conference on Advances in Engineering, Science and Technology - 2021 , May 2021, URL: https://www.ijtsrd.com/papers/ijtsrd42456.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/42456/brain-tumor-detection-using-mri-images/deepa-dangwal
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
MEDICAL IMAGES AUTHENTICATION THROUGH WATERMARKING PRESERVING ROIhiij
Telemedicine is a well-known application where enormous amount of medical data need to be securely
transferred over the public network and manipulate effectively. Medical image watermarking is an
appropriate method used for enhancing security and authentication of medical data, which is crucial and
used for further diagnosis and reference. This project focuses on the study of medical image
watermarking methods for protecting and authenticating medical data. Additionally, it covers algorithm
for application of water marking technique on Region of Non Interest (RONI) of the medical image
preserving Region of Interest (ROI). The medical images can be transferred securely by embedding
watermarks in RONI allowing verification of the legitimate changes at the receiving end without affecting
ROI. Segmentation plays an important role in medical image processing for separating the ROI from
medical image. The proposed system separate the ROI from medical image by GUI based approach,
which works for all types of medical images. The experimental results show the satisfactory performance
of the system to authenticate the medical images preserving ROI.
Framework for comprehensive enhancement of brain tumor images with single-win...IJECEIAES
Usage of grayscale format of radiological images is proportionately more as compared to that of colored one. This format of medical image suffers from all the possibility of improper clinical inference which will lead to error-prone analysis in further usage of such images in disease detection or classification. Therefore, we present a framework that offers single-window operation with a set of image enhancing algorithm meant for further optimizing the visuality of medical images. The framework performs preliminary pre-processing operation followed by implication of linear and non-linear filter and multi-level image enhancement processes. The significant contribution of this study is that it offers a comprehensive mechanism to implement the various enhancement schemes in highly discrete way that offers potential flexibility to physical in order to draw clinical conclusion about the disease being monitored. The proposed system takes the case study of brain tumor to implement to testify the framework.
Artificial intelligence in medical image processingFarzad Jahedi
Artificial intelligence in medical image processing shows promise to help radiologists in three key ways:
1) AI algorithms can analyze millions of current medical journals and cross-reference symptoms from cancer patients to make hypotheses and assist in decision making.
2) Image processing and segmentation techniques using artificial neural networks, fuzzy logic, and other methods can help analyze medical images like MRI, CT, ultrasound and more to identify patterns and help diagnose conditions.
3) Hybrid intelligent systems combine approaches like neural networks and genetic algorithms to automatically train systems and generate architectures to further improve analysis of medical images and decision support.
deep learning applications in medical image analysis brain tumorVenkat Projects
The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the _eld. The advantage of machine learning in an era of medical big data is that signi_cant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classi_cation, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
Medical Image Processing in Nuclear Medicine and Bone ArthroplastyIOSR Journals
This document discusses medical image processing in nuclear medicine and bone arthroplasty. It provides background on nuclear medicine imaging techniques like planar imaging, SPECT, PET and hybrid SPECT/CT and PET/CT systems. It then discusses how MATLAB can be used for medical image processing tasks in nuclear medicine like organ contouring, interpolation, filtering, segmentation, background removal, registration and volume quantification. Specific examples of nuclear medicine examinations that can be analyzed using MATLAB algorithms are also mentioned.
Lec1: Medical Image Computing - Introduction Ulaş Bağcı
2017 Spring, UCF Medical Image Computing Course
Basics of Radiological Image Modalities and their clinical use (MRI, PET, CT, fMRI, DTI, ...)
• Introduction to Medical Image Computing and Toolkits
• Image Filtering, Enhancement, Noise Reduction, and
Signal Processing
• MedicalImageRegistration
• MedicalImageSegmentation
• MedicalImageVisualization
• Machine Learning in Medical Imaging
• Shape Modeling/Analysis of Medical Images
Deep Learning in Radiology
Brain Tumor Detection using MRI ImagesYogeshIJTSRD
Brain tumor segmentation is a very important task in medical image processing. Early diagnosis of brain tumors plays a crucial role in improving treatment possibilities and increases the survival rate of the patients. For the study of tumor detection and segmentation, MRI Images are very useful in recent years. One of the foremost crucial tasks in any brain tumor detection system is that the detachment of abnormal tissues from normal brain tissues. Because of MRI Images, we will detect the brain tumor. Detection of unusual growth of tissues and blocks of blood within the system is seen in an MRI Imaging. Brain tumor detection using MRI images may be a challenging task due to the brains complex structure.In this paper, we propose an image segmentation method to detect tumors from MRI images using an interface of GUI in MATLAB. The method of distinguishing brain tumors through MRI images is often sorted into four sections of image processing as pre processing, feature extraction, image segmentation, and image classification. During this paper, weve used various algorithms for the partial fulfillment of the necessities to hit the simplest results that may help us to detect brain tumors within the early stage. Deepa Dangwal | Aditya Nautiyal | Dakshita Adhikari | Kapil Joshi "Brain Tumor Detection using MRI Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Conference on Advances in Engineering, Science and Technology - 2021 , May 2021, URL: https://www.ijtsrd.com/papers/ijtsrd42456.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/42456/brain-tumor-detection-using-mri-images/deepa-dangwal
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
MEDICAL IMAGES AUTHENTICATION THROUGH WATERMARKING PRESERVING ROIhiij
Telemedicine is a well-known application where enormous amount of medical data need to be securely
transferred over the public network and manipulate effectively. Medical image watermarking is an
appropriate method used for enhancing security and authentication of medical data, which is crucial and
used for further diagnosis and reference. This project focuses on the study of medical image
watermarking methods for protecting and authenticating medical data. Additionally, it covers algorithm
for application of water marking technique on Region of Non Interest (RONI) of the medical image
preserving Region of Interest (ROI). The medical images can be transferred securely by embedding
watermarks in RONI allowing verification of the legitimate changes at the receiving end without affecting
ROI. Segmentation plays an important role in medical image processing for separating the ROI from
medical image. The proposed system separate the ROI from medical image by GUI based approach,
which works for all types of medical images. The experimental results show the satisfactory performance
of the system to authenticate the medical images preserving ROI.
Framework for comprehensive enhancement of brain tumor images with single-win...IJECEIAES
Usage of grayscale format of radiological images is proportionately more as compared to that of colored one. This format of medical image suffers from all the possibility of improper clinical inference which will lead to error-prone analysis in further usage of such images in disease detection or classification. Therefore, we present a framework that offers single-window operation with a set of image enhancing algorithm meant for further optimizing the visuality of medical images. The framework performs preliminary pre-processing operation followed by implication of linear and non-linear filter and multi-level image enhancement processes. The significant contribution of this study is that it offers a comprehensive mechanism to implement the various enhancement schemes in highly discrete way that offers potential flexibility to physical in order to draw clinical conclusion about the disease being monitored. The proposed system takes the case study of brain tumor to implement to testify the framework.
Artificial intelligence in medical image processingFarzad Jahedi
Artificial intelligence in medical image processing shows promise to help radiologists in three key ways:
1) AI algorithms can analyze millions of current medical journals and cross-reference symptoms from cancer patients to make hypotheses and assist in decision making.
2) Image processing and segmentation techniques using artificial neural networks, fuzzy logic, and other methods can help analyze medical images like MRI, CT, ultrasound and more to identify patterns and help diagnose conditions.
3) Hybrid intelligent systems combine approaches like neural networks and genetic algorithms to automatically train systems and generate architectures to further improve analysis of medical images and decision support.
deep learning applications in medical image analysis brain tumorVenkat Projects
The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the _eld. The advantage of machine learning in an era of medical big data is that signi_cant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classi_cation, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
Integrated Modelling Approach for Enhancing Brain MRI with Flexible Pre-Proce...IJECEIAES
The assurance of an information quality of the input medical image is a critical step to offer highly precise and reliable diagnosis of clinical condition in human. The importance of such assurance becomes more while dealing with important organ like brain. Magnetic Resonance Imaging (MRI) is one of the most trusted mediums to investigate brain. Looking into the existing trends of investigating brain MRI, it was observed that researchers are more prone to investigate advanced problems e.g. segmentation, localization, classification, etc considering image dataset. There is less work carried out towards image preprocessing that potential affects the later stage of diagnosing. Therefore, this paper introduces a novel model of integrated image enhancement algorithm that is capable of solving different and discrete problems of performing image pre-processing for offering highly improved and enhanced brain MRI. The comparative outcomes exhibit the advantage of its simplistic implemetation strategy.
This document summarizes current medical image processing research being conducted at various universities. It describes projects involving ECG compression, MRI using blood oxygen level detection, spinal image fusion to improve diagnosis, segmenting anatomical structures from MRI, and using elasticity imaging to detect kidney transplant rejection. It also lists programs for processing MRI data and retinex image processing, as well as websites with medical image test data and news about diagnostic imaging.
MRI Image Segmentation Using Gradient Based Watershed Transform In Level Set ...IJERA Editor
This document summarizes a research paper on segmenting MRI brain images using a gradient-based watershed transform within a level set method. The paper begins with an introduction on the importance of accurate brain image segmentation for medical diagnosis. It then reviews existing segmentation methods and their limitations. The proposed method uses a two-level gradient watershed transform combined with morphological operations within a level set framework to segment brain images. Experimental results showed this approach achieved better segmentation accuracy than traditional methods.
Neuroendoscopy Adapter Module Development for Better Brain Tumor Image Visual...IJECEIAES
The issue of brain magnetic resonance image exploration together with classification receives a significant awareness in recent years. Indeed, various computer-aided-diagnosis solutions were suggested to support radiologist in decision-making. In this circumstance, adequate image classification is extremely required as it is the most common critical brain tumors which often develop from subdural hematoma cells, which might be common type in adults. In healthcare milieu, brain MRIs are intended for identification of tumor. In this regard, various computerized diagnosis systems were suggested to help medical professionals in clinical decision-making. As per recent problems, Neuroendoscopy is the gold standard intended for discovering brain tumors; nevertheless, typical Neuroendoscopy can certainly overlook ripped growths. Neuroendoscopy is a minimally-invasive surgical procedure in which the neurosurgeon removes the tumor through small holes in the skull or through the mouth or nose. Neuroendoscopy enables neurosurgeons to access areas of the brain that cannot be reached with traditional surgery to remove the tumor without cutting or harming other parts of the skull. We focused on finding out whether or not visual images of tumor ripped lesions ended up being much better by auto fluorescence image resolution as well as narrow-band image resolution graphic evaluation jointly with the latest neuroendoscopy technique. Also, within the last several years, pathology labs began to proceed in the direction of an entirely digital workflow, using the electronic slides currently being the key element of this technique. Besides lots of benefits regarding storage as well as exploring capabilities with the image information, among the benefits of electronic slides is that they can help the application of image analysis approaches which seek to develop quantitative attributes to assist pathologists in their work. However, systems also have some difficulties in execution and handling. Hence, such conventional method needs automation. We developed and employed to look for the targeted importance along with uncovering the best-focused graphic position by way of aliasing search method incorporated with new Neuroendoscopy Adapter Module (NAM) technique.
During past few years, brain tumor segmentation in CT has become an emergent research area in the field of medical imaging system. Brain tumor detection helps in finding the exact size and location of tumor. An efficient algorithm is proposed in this project for tumor detection based on segmentation and morphological operators. Firstly quality of scanned image is enhanced and then morphological operators are applied to detect the tumor in the scanned image. The problem with biopsy is that the patient has to be hospitalized and also the results (around 15%) give false negative. Scan images are read by radiologist but it's a subjective analysis which requires more experience. In the proposed work we segment the renal region and then classify the tumors as benign or malignant by using ANFIS, which is a non-invasive automated process. This approach reduces the waiting time of the patient.
Medical image processing involves acquiring medical images through modalities like X-rays, CT, MRI, using techniques like ultrasound. The images are then preprocessed, segmented, analyzed and classified to diagnose diseases or detect abnormalities. Key applications include tumor detection, monitoring bone strength, and medical image fusion to enable accurate analysis and remote sharing of data to enhance diagnosis and treatment.
This document discusses medical image processing and its application to breast cancer detection. It provides an overview of digital image processing techniques used in medical imaging like X-rays, mammography, ultrasound, MRI and CT. Computer-aided diagnosis (CAD) helps in tasks like visualization, detection, localization, segmentation and classification of medical images. For breast cancer detection specifically, the document discusses mammography and challenges in detecting tumors in dense breast tissue. It also reviews several published methods for segmenting and analyzing lesions in mammograms and evaluates their performance based on parameters like true positives, false positives, etc.
Model guided therapy and the role of dicom in surgeryKlaus19
1. Model-guided therapy uses patient-specific models to complement image-guided therapy, bringing treatment closer to precise diagnosis, accurate prognosis assessment, and individualized planning and validation of therapy.
2. TIMMS is an IT system that facilitates model-guided therapy through interoperability of data, images, models, and tools to support the therapeutic intervention.
3. Patient-specific models in TIMMS must represent multidimensional and multiscale patient data, interface various system components, and link model components meaningfully while maintaining model accuracy over time.
Identifying brain tumour from mri image using modified fcm and supportIAEME Publication
This document summarizes a research paper that proposes a technique for identifying brain tumors in MRI images. The technique involves 4 steps: 1) preprocessing the MRI image, 2) segmenting the image using a modified fuzzy C-means algorithm, 3) extracting features from the segmented regions like mean, standard deviation, and pixel orientation, and 4) classifying the image as tumorous or normal using support vector machine classification on the extracted features. The technique is evaluated on MRI brain images and achieves a testing accuracy of 93%, demonstrating its effectiveness at detecting brain tumors compared to other segmentation and classification methods.
DETECTING BRAIN TUMOUR FROM MRI IMAGE USING MATLAB GUI PROGRAMMEIJCSES Journal
Engineers have been actively developing tools to detect tumors and to process medical images. Medical image segmentation is a powerful tool that is often used to detect tumors. Many scientists and researchers are working to develop and add more features to this tool. This project is about detecting Brain tumors from MRI images using an interface of GUI in Matlab. Using the GUI, this program can use various combinations of segmentation, filters, and other image processing algorithms to achieve the best results.
We start with filtering the image using Prewitt horizontal edge-emphasizing filter. The next step for detecting tumor is "watershed pixels." The most important part of this project is that all the Matlab programs work with GUI “Matlab guide”. This allows us to use various combinations of filters, and other
image processing techniques to arrive at the best result that can help us detect brain tumors in their early stages.
Brain Tumor is basically the unusual growth of some new cells found in the brain. This can happen in any area of the brain. Tumor are categorized by finding the origin of the cell which has tumor and if the cells are cancerous or not. Segmentation process is carried out to find if brain tumor exists or not, then the response of the patient to the tests performed is collected, different therapy sessions and also by creating models which has tumor growth in it. This one is different from the other types of tumor. Anyone can suffer from this disease. Primary tumors are basically Benign or Malignant. Here, we propose CNN Convolutional Neural Network based approach for improving accuracy. It also have capacity to detect certain features without any interaction from human beings. With the help of this model it classifies whether the MRI brain scan has tumor or not. There are other different algorithms, but this paper shows that CNN gives more accuracy than the rest. This model gives validation accuracy between 77 85 . gives more precise and accurate results. CNN also let us to train large data sets and cross validate results, hence the most easy and reliable model to use. Anagha Jayakumar | Mehtab Mehdi "Brain Tumor Detection using Neural Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38105.pdf Paper URL : https://www.ijtsrd.com/computer-science/other/38105/brain-tumor-detection-using-neural-network/anagha-jayakumar
IRJET - Machine Learning Applications on Cancer Prognosis and PredictionIRJET Journal
This document discusses machine learning applications for cancer prognosis and prediction using MRI images. It presents a methodology for detecting brain tumors from MRI reports using image segmentation in MATLAB. The key steps include pre-processing MRI images, segmenting the tumor area using algorithms like fuzzy C-means and watershed, extracting features from the tumor region, and classifying tumors as benign or malignant. The proposed system achieved encouraging results for accuracy and precision in automatic brain tumor detection and classification. Future work may involve classifying tumor types and monitoring tumor growth over time using sequential patient images.
Mri brain tumour detection by histogram and segmentationiaemedu
This document summarizes a research paper on detecting brain tumors in MRI images using a combination of histogram thresholding, modified gradient vector field (GVF), and morphological operators. The non-brain regions are removed using morphological operators. Histogram thresholding is then used to detect if the brain is normal or abnormal/contains a tumor. If abnormal, the modified GVF is used to detect the tumor contour. The proposed method aims to be computationally efficient by only performing segmentation if a tumor is detected. It was tested on many MRI brain images and performance was validated against human expert segmentation.
INVESTIGATION THE EFFECT OF USING GRAY LEVEL AND RGB CHANNELS ON BRAIN TUMOR ...csandit
Analysis the effect of using gray level on the Brain tumor image for improving speed of object
detection in the field of Medical Image using image processing technique. Specific areas of
interest are image binarization method, Image segmentation. Experiments will be performed by
image processing using Matlab. This paper presents a strategy for decreasing the calculation
time by using gray level and just one channel Red or Green or Blue in medical Image and
analysis its impact in order to improve detection time and the main goal is to reduce time
complexity.
Brain tumor detection and localization in magnetic resonance imagingijitcs
A tumor also known as neoplasm is a growth in the abnormal tissue which can be differentiated from the
surrounding tissue by its structure. A tumor may lead to cancer, which is a major leading cause of death and
responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate
in the world. Great knowledge and experience on radiology are required for accurate tumor detection in
medical imaging. Automation of tumor detection is required because there might be a shortage of skilled
radiologists at a time of great need. We propose an automatic brain tumor detectionand localization
framework that can detect and localize brain tumor in magnetic resonance imaging. The proposed brain
tumor detection and localization framework comprises five steps: image acquisition, pre-processing, edge
detection, modified histogram clustering and morphological operations. After morphological operations,
tumors appear as pure white color on pure black backgrounds. We used 50 neuroimages to optimize our
system and 100 out-of-sample neuroimages to test our system. The proposed tumor detection and localization
system was found to be able to accurately detect and localize brain tumor in magnetic resonance imaging.
The preliminary results demonstrate how a simple machine learning classifier with a set of simple
image-based features can result in high classification accuracy. The preliminary results also demonstrate the
efficacy and efficiency of our five-step brain tumor detection and localization approach and motivate us to
extend this framework to detect and localize a variety of other types of tumors in other types of medical
imagery.
Optimizing Problem of Brain Tumor Detection using Image ProcessingIRJET Journal
This document summarizes several existing methods for detecting brain tumors using magnetic resonance imaging (MRI). It discusses techniques such as image preprocessing, segmentation, feature extraction, and classification methods. Specifically, it reviews 10 different papers that propose various approaches for brain tumor detection, segmentation, and classification. These include using k-means clustering, fuzzy c-means, probabilistic neural networks, support vector machines, genetic algorithms, and sparse representation classification. The goal is to evaluate and compare different existing methods for automated brain tumor detection and analysis using MRI images.
Classification of Abnormalities in Brain MRI Images Using PCA and SVMIJERA Editor
The impact of digital image processing is increasing by the day for its use in the medical and research areas. Medical image classification scheme has been on the increase in order to help physicians and medical practitioners in their evaluation and analysis of diseases. Several classification schemes such as Artificial Neural Network (ANN), Bayes Classification, Support Vector Machine (SVM) and K-Means Nearest Neighbor have been used. In this paper, we evaluate and compared the performance of SVM and PCA by analyzing diseased image of the brain (Alzheimer) and normal (MRI) brain. The results show that Principal Components Analysis outperforms the Support Vector Machine in terms of training time and recognition time.
Improving radiologists’ and orthopedists’ QoE in diagnosing lumbar disk herni...IJECEIAES
This document describes research on improving radiologists' and orthopedists' quality of experience (QoE) in diagnosing lumbar disk herniation using 3D modeling. The researchers built 3D models from MRI scans and developed an automated diagnosis framework. They evaluated the 3D models on 14 medical specialists and found it increased their QoE in 95% of cases. The automated framework was trained on 83 cases and tested on new cases, achieving 90% diagnosis accuracy.
The document describes a study that aimed to reconstruct the 3D structure of the tibial nerve through micro-CT imaging. Tibial nerve samples were stained with calcium chloride and scanned with micro-CT to obtain 2D images. The nerve bundle contours were then extracted from these images using an automated algorithm. This allowed for the successful construction of a 3D model of the tibial nerve bundles. The 3D reconstruction provides detailed visualization of the nerve's internal structure and geometry. This technique is an improvement over previous methods and lays the foundation for further research on peripheral nerve anatomy and repair.
An optimized approach for extensive segmentation and classification of brain ...IJECEIAES
With the significant contribution in medical image processing for an effective diagnosis of critical health condition in human, there has been evolution of various methods and techniques in abnormality detection and classification process. An insight to the existing approaches highlights that potential amount of work is being carried out in detection and segmentation process but less effective modelling towards classification problems. This manuscript discusses about a simple and robust modelling of a technique that offers comprehensive segmentation process as well as classification process using Artificial Neural Network. Different from any existing approach, the study offers more granularities towards foreground/ background indexing with its comprehensive segmentation process while introducing a unique morphological operation along with graph-believe network for ensuring approximately 99% of accuracy of proposed system in contrast to existing learning scheme.
Perplexity of Index Models over Evolving Linked Data Thomas Gottron
ESWC presentation on the stability of 12 different index models for linked data. Provides a formalisation of the index models as well as stability evaluation based on data distributions and information theoretic metrics.
Integrated Modelling Approach for Enhancing Brain MRI with Flexible Pre-Proce...IJECEIAES
The assurance of an information quality of the input medical image is a critical step to offer highly precise and reliable diagnosis of clinical condition in human. The importance of such assurance becomes more while dealing with important organ like brain. Magnetic Resonance Imaging (MRI) is one of the most trusted mediums to investigate brain. Looking into the existing trends of investigating brain MRI, it was observed that researchers are more prone to investigate advanced problems e.g. segmentation, localization, classification, etc considering image dataset. There is less work carried out towards image preprocessing that potential affects the later stage of diagnosing. Therefore, this paper introduces a novel model of integrated image enhancement algorithm that is capable of solving different and discrete problems of performing image pre-processing for offering highly improved and enhanced brain MRI. The comparative outcomes exhibit the advantage of its simplistic implemetation strategy.
This document summarizes current medical image processing research being conducted at various universities. It describes projects involving ECG compression, MRI using blood oxygen level detection, spinal image fusion to improve diagnosis, segmenting anatomical structures from MRI, and using elasticity imaging to detect kidney transplant rejection. It also lists programs for processing MRI data and retinex image processing, as well as websites with medical image test data and news about diagnostic imaging.
MRI Image Segmentation Using Gradient Based Watershed Transform In Level Set ...IJERA Editor
This document summarizes a research paper on segmenting MRI brain images using a gradient-based watershed transform within a level set method. The paper begins with an introduction on the importance of accurate brain image segmentation for medical diagnosis. It then reviews existing segmentation methods and their limitations. The proposed method uses a two-level gradient watershed transform combined with morphological operations within a level set framework to segment brain images. Experimental results showed this approach achieved better segmentation accuracy than traditional methods.
Neuroendoscopy Adapter Module Development for Better Brain Tumor Image Visual...IJECEIAES
The issue of brain magnetic resonance image exploration together with classification receives a significant awareness in recent years. Indeed, various computer-aided-diagnosis solutions were suggested to support radiologist in decision-making. In this circumstance, adequate image classification is extremely required as it is the most common critical brain tumors which often develop from subdural hematoma cells, which might be common type in adults. In healthcare milieu, brain MRIs are intended for identification of tumor. In this regard, various computerized diagnosis systems were suggested to help medical professionals in clinical decision-making. As per recent problems, Neuroendoscopy is the gold standard intended for discovering brain tumors; nevertheless, typical Neuroendoscopy can certainly overlook ripped growths. Neuroendoscopy is a minimally-invasive surgical procedure in which the neurosurgeon removes the tumor through small holes in the skull or through the mouth or nose. Neuroendoscopy enables neurosurgeons to access areas of the brain that cannot be reached with traditional surgery to remove the tumor without cutting or harming other parts of the skull. We focused on finding out whether or not visual images of tumor ripped lesions ended up being much better by auto fluorescence image resolution as well as narrow-band image resolution graphic evaluation jointly with the latest neuroendoscopy technique. Also, within the last several years, pathology labs began to proceed in the direction of an entirely digital workflow, using the electronic slides currently being the key element of this technique. Besides lots of benefits regarding storage as well as exploring capabilities with the image information, among the benefits of electronic slides is that they can help the application of image analysis approaches which seek to develop quantitative attributes to assist pathologists in their work. However, systems also have some difficulties in execution and handling. Hence, such conventional method needs automation. We developed and employed to look for the targeted importance along with uncovering the best-focused graphic position by way of aliasing search method incorporated with new Neuroendoscopy Adapter Module (NAM) technique.
During past few years, brain tumor segmentation in CT has become an emergent research area in the field of medical imaging system. Brain tumor detection helps in finding the exact size and location of tumor. An efficient algorithm is proposed in this project for tumor detection based on segmentation and morphological operators. Firstly quality of scanned image is enhanced and then morphological operators are applied to detect the tumor in the scanned image. The problem with biopsy is that the patient has to be hospitalized and also the results (around 15%) give false negative. Scan images are read by radiologist but it's a subjective analysis which requires more experience. In the proposed work we segment the renal region and then classify the tumors as benign or malignant by using ANFIS, which is a non-invasive automated process. This approach reduces the waiting time of the patient.
Medical image processing involves acquiring medical images through modalities like X-rays, CT, MRI, using techniques like ultrasound. The images are then preprocessed, segmented, analyzed and classified to diagnose diseases or detect abnormalities. Key applications include tumor detection, monitoring bone strength, and medical image fusion to enable accurate analysis and remote sharing of data to enhance diagnosis and treatment.
This document discusses medical image processing and its application to breast cancer detection. It provides an overview of digital image processing techniques used in medical imaging like X-rays, mammography, ultrasound, MRI and CT. Computer-aided diagnosis (CAD) helps in tasks like visualization, detection, localization, segmentation and classification of medical images. For breast cancer detection specifically, the document discusses mammography and challenges in detecting tumors in dense breast tissue. It also reviews several published methods for segmenting and analyzing lesions in mammograms and evaluates their performance based on parameters like true positives, false positives, etc.
Model guided therapy and the role of dicom in surgeryKlaus19
1. Model-guided therapy uses patient-specific models to complement image-guided therapy, bringing treatment closer to precise diagnosis, accurate prognosis assessment, and individualized planning and validation of therapy.
2. TIMMS is an IT system that facilitates model-guided therapy through interoperability of data, images, models, and tools to support the therapeutic intervention.
3. Patient-specific models in TIMMS must represent multidimensional and multiscale patient data, interface various system components, and link model components meaningfully while maintaining model accuracy over time.
Identifying brain tumour from mri image using modified fcm and supportIAEME Publication
This document summarizes a research paper that proposes a technique for identifying brain tumors in MRI images. The technique involves 4 steps: 1) preprocessing the MRI image, 2) segmenting the image using a modified fuzzy C-means algorithm, 3) extracting features from the segmented regions like mean, standard deviation, and pixel orientation, and 4) classifying the image as tumorous or normal using support vector machine classification on the extracted features. The technique is evaluated on MRI brain images and achieves a testing accuracy of 93%, demonstrating its effectiveness at detecting brain tumors compared to other segmentation and classification methods.
DETECTING BRAIN TUMOUR FROM MRI IMAGE USING MATLAB GUI PROGRAMMEIJCSES Journal
Engineers have been actively developing tools to detect tumors and to process medical images. Medical image segmentation is a powerful tool that is often used to detect tumors. Many scientists and researchers are working to develop and add more features to this tool. This project is about detecting Brain tumors from MRI images using an interface of GUI in Matlab. Using the GUI, this program can use various combinations of segmentation, filters, and other image processing algorithms to achieve the best results.
We start with filtering the image using Prewitt horizontal edge-emphasizing filter. The next step for detecting tumor is "watershed pixels." The most important part of this project is that all the Matlab programs work with GUI “Matlab guide”. This allows us to use various combinations of filters, and other
image processing techniques to arrive at the best result that can help us detect brain tumors in their early stages.
Brain Tumor is basically the unusual growth of some new cells found in the brain. This can happen in any area of the brain. Tumor are categorized by finding the origin of the cell which has tumor and if the cells are cancerous or not. Segmentation process is carried out to find if brain tumor exists or not, then the response of the patient to the tests performed is collected, different therapy sessions and also by creating models which has tumor growth in it. This one is different from the other types of tumor. Anyone can suffer from this disease. Primary tumors are basically Benign or Malignant. Here, we propose CNN Convolutional Neural Network based approach for improving accuracy. It also have capacity to detect certain features without any interaction from human beings. With the help of this model it classifies whether the MRI brain scan has tumor or not. There are other different algorithms, but this paper shows that CNN gives more accuracy than the rest. This model gives validation accuracy between 77 85 . gives more precise and accurate results. CNN also let us to train large data sets and cross validate results, hence the most easy and reliable model to use. Anagha Jayakumar | Mehtab Mehdi "Brain Tumor Detection using Neural Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38105.pdf Paper URL : https://www.ijtsrd.com/computer-science/other/38105/brain-tumor-detection-using-neural-network/anagha-jayakumar
IRJET - Machine Learning Applications on Cancer Prognosis and PredictionIRJET Journal
This document discusses machine learning applications for cancer prognosis and prediction using MRI images. It presents a methodology for detecting brain tumors from MRI reports using image segmentation in MATLAB. The key steps include pre-processing MRI images, segmenting the tumor area using algorithms like fuzzy C-means and watershed, extracting features from the tumor region, and classifying tumors as benign or malignant. The proposed system achieved encouraging results for accuracy and precision in automatic brain tumor detection and classification. Future work may involve classifying tumor types and monitoring tumor growth over time using sequential patient images.
Mri brain tumour detection by histogram and segmentationiaemedu
This document summarizes a research paper on detecting brain tumors in MRI images using a combination of histogram thresholding, modified gradient vector field (GVF), and morphological operators. The non-brain regions are removed using morphological operators. Histogram thresholding is then used to detect if the brain is normal or abnormal/contains a tumor. If abnormal, the modified GVF is used to detect the tumor contour. The proposed method aims to be computationally efficient by only performing segmentation if a tumor is detected. It was tested on many MRI brain images and performance was validated against human expert segmentation.
INVESTIGATION THE EFFECT OF USING GRAY LEVEL AND RGB CHANNELS ON BRAIN TUMOR ...csandit
Analysis the effect of using gray level on the Brain tumor image for improving speed of object
detection in the field of Medical Image using image processing technique. Specific areas of
interest are image binarization method, Image segmentation. Experiments will be performed by
image processing using Matlab. This paper presents a strategy for decreasing the calculation
time by using gray level and just one channel Red or Green or Blue in medical Image and
analysis its impact in order to improve detection time and the main goal is to reduce time
complexity.
Brain tumor detection and localization in magnetic resonance imagingijitcs
A tumor also known as neoplasm is a growth in the abnormal tissue which can be differentiated from the
surrounding tissue by its structure. A tumor may lead to cancer, which is a major leading cause of death and
responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate
in the world. Great knowledge and experience on radiology are required for accurate tumor detection in
medical imaging. Automation of tumor detection is required because there might be a shortage of skilled
radiologists at a time of great need. We propose an automatic brain tumor detectionand localization
framework that can detect and localize brain tumor in magnetic resonance imaging. The proposed brain
tumor detection and localization framework comprises five steps: image acquisition, pre-processing, edge
detection, modified histogram clustering and morphological operations. After morphological operations,
tumors appear as pure white color on pure black backgrounds. We used 50 neuroimages to optimize our
system and 100 out-of-sample neuroimages to test our system. The proposed tumor detection and localization
system was found to be able to accurately detect and localize brain tumor in magnetic resonance imaging.
The preliminary results demonstrate how a simple machine learning classifier with a set of simple
image-based features can result in high classification accuracy. The preliminary results also demonstrate the
efficacy and efficiency of our five-step brain tumor detection and localization approach and motivate us to
extend this framework to detect and localize a variety of other types of tumors in other types of medical
imagery.
Optimizing Problem of Brain Tumor Detection using Image ProcessingIRJET Journal
This document summarizes several existing methods for detecting brain tumors using magnetic resonance imaging (MRI). It discusses techniques such as image preprocessing, segmentation, feature extraction, and classification methods. Specifically, it reviews 10 different papers that propose various approaches for brain tumor detection, segmentation, and classification. These include using k-means clustering, fuzzy c-means, probabilistic neural networks, support vector machines, genetic algorithms, and sparse representation classification. The goal is to evaluate and compare different existing methods for automated brain tumor detection and analysis using MRI images.
Classification of Abnormalities in Brain MRI Images Using PCA and SVMIJERA Editor
The impact of digital image processing is increasing by the day for its use in the medical and research areas. Medical image classification scheme has been on the increase in order to help physicians and medical practitioners in their evaluation and analysis of diseases. Several classification schemes such as Artificial Neural Network (ANN), Bayes Classification, Support Vector Machine (SVM) and K-Means Nearest Neighbor have been used. In this paper, we evaluate and compared the performance of SVM and PCA by analyzing diseased image of the brain (Alzheimer) and normal (MRI) brain. The results show that Principal Components Analysis outperforms the Support Vector Machine in terms of training time and recognition time.
Improving radiologists’ and orthopedists’ QoE in diagnosing lumbar disk herni...IJECEIAES
This document describes research on improving radiologists' and orthopedists' quality of experience (QoE) in diagnosing lumbar disk herniation using 3D modeling. The researchers built 3D models from MRI scans and developed an automated diagnosis framework. They evaluated the 3D models on 14 medical specialists and found it increased their QoE in 95% of cases. The automated framework was trained on 83 cases and tested on new cases, achieving 90% diagnosis accuracy.
The document describes a study that aimed to reconstruct the 3D structure of the tibial nerve through micro-CT imaging. Tibial nerve samples were stained with calcium chloride and scanned with micro-CT to obtain 2D images. The nerve bundle contours were then extracted from these images using an automated algorithm. This allowed for the successful construction of a 3D model of the tibial nerve bundles. The 3D reconstruction provides detailed visualization of the nerve's internal structure and geometry. This technique is an improvement over previous methods and lays the foundation for further research on peripheral nerve anatomy and repair.
An optimized approach for extensive segmentation and classification of brain ...IJECEIAES
With the significant contribution in medical image processing for an effective diagnosis of critical health condition in human, there has been evolution of various methods and techniques in abnormality detection and classification process. An insight to the existing approaches highlights that potential amount of work is being carried out in detection and segmentation process but less effective modelling towards classification problems. This manuscript discusses about a simple and robust modelling of a technique that offers comprehensive segmentation process as well as classification process using Artificial Neural Network. Different from any existing approach, the study offers more granularities towards foreground/ background indexing with its comprehensive segmentation process while introducing a unique morphological operation along with graph-believe network for ensuring approximately 99% of accuracy of proposed system in contrast to existing learning scheme.
Perplexity of Index Models over Evolving Linked Data Thomas Gottron
ESWC presentation on the stability of 12 different index models for linked data. Provides a formalisation of the index models as well as stability evaluation based on data distributions and information theoretic metrics.
This document summarizes research on distributed path computation algorithms that aim to prevent routing loops. It introduces the Distributed Path Computation with Intermediate Variables (DIV) algorithm, which can operate with any routing algorithm to guarantee loop-freedom. DIV generalizes previous loop-free algorithms and provably outperforms them by reducing synchronous updates and helping maintain paths during network changes. The document also reviews link-state routing, distance-vector routing, and existing loop-prevention techniques like the Diffusing Update Algorithm and Loop Free Invariance algorithms.
Este documento presenta las secciones clave para desarrollar un proyecto de investigación, incluyendo la identificación del problema, la formulación de objetivos generales y específicos, el marco teórico, la hipótesis o supuesto, las variables, la población o muestra, las técnicas e instrumentos de recolección de datos, y los procedimientos y análisis de datos.
The document describes a promotional video for Atom Experience HQ. Scientists develop a magic formula that transforms a dull man into a handsome hunk. He goes on an exciting ride with a woman on a Segway and then a convertible. Later, he races cars and goes head-to-head with an F1 racer, only to discover the racer is the same woman, in a surprise ending. The video aims to showcase Atom Experience's products and services in an entertaining fashion.
1) DLC films were grown on silicon substrates using a microwave plasma CVD system with different negative dc biases applied to the substrate holder. Raman spectroscopy showed the films had characteristic D and G bands of DLC and that the sp3 content changed with bias.
2) The DLC films were irradiated with 130 MeV Ni ions to doses of 3e11, 3e12, and 1e13 ions/cm2. Raman spectroscopy showed the irradiation increased sp2 bonding due to local heating from the ions.
3) Atomic force microscopy found the surface roughness of the DLC films decreased after irradiation compared to the pristine films. This is because the electronic energy loss of the 130 MeV Ni ions was below
This document proposes a scheme for public verifiability in cloud computing using signcryption based on elliptic curves. The key components of the proposed system include users, a cloud service provider, an authentication server, and a certificate authority. The scheme relies on erasure-correcting codes to distribute and redundantly store user data across multiple cloud servers. It uses signcryption/unsigncryption based on elliptic curves to generate verification tokens for the stored data and enable public verifiability, allowing an authentication server to verify the integrity and accuracy of user data on cloud servers without involving the user. The scheme aims to simultaneously detect any data errors and identify the misbehaving servers upon verification.
The document discusses building a personal brand through 4 steps: 1) Determine your appeal by listing descriptive words for your personality and qualities. 2) Determine your description by developing a descriptive modifier. 3) Determine your function by writing what you do or will do in your career. 4) Put it all together into a short phrase or sentence no more than 5 words that combines the previous lists. Developing a personal brand enhances self-awareness, narrows goals, helps one stand out, and breathes new life into career documents like resumes and LinkedIn profiles.
Making Use of the Linked Data Cloud: The Role of Index StructuresThomas Gottron
The intensive growth of the Linked Open Data Cloud has spawned a web of data where a multitude of data sources provides huge amounts of valuable information across different domains. Nowadays, when accessing and using Linked Data more and more often the challenging question is not so much whether there is relevant data available, but rather where it can be found and how it is structured. Thus, index structures play an important role for making use of the information in LOD cloud. In this talk I will address three aspects of Linked Data index structures: (1) a high level view and categorization of indices structures and how they can be queried and explored, (2) approaches for building index structures and the need to maintain them and (3) some example applications which greatly benefit from indices over linked data.
Image Processing for Automated Flaw Detection and CMYK model for Color Image ...IOSR Journals
This document discusses several image processing techniques for automated flaw detection in infrared thermography data, including thermal contrast computation, pulsed phase thermography, thermographic signal reconstruction, and principal component analysis. It also discusses using type-2 fuzzy logic to model uncertainties in image segmentation and classification by representing membership functions as three-dimensional rather than two-dimensional. The goal is to develop robust and automated techniques for flaw detection and sizing in carbon fiber composite materials using infrared thermography.
This document summarizes the finance award for Term 14-15 at LC FTU HCMC. It outlines improvements made to finance policies and procedures through legislative meetings, increased financial reporting through monthly budget submissions and public reports, enhanced auditing through meetings between finance teams, strong financial sustainability metrics, and innovative initiatives around online accounting and contributing to national projects.
Studying the Impact of the Solar Activity on the Maximum Usable Frequency Pa...IOSR Journals
This study analyzed the impact of solar activity on the Maximum Usable Frequency (MUF) parameter over Iraq in 2000 and 2010 using the VOACAP model. The results showed that solar activity in 2000, which was the peak of solar cycle 23, had a stronger influence on MUF values than in 2010, which was the minimum of solar cycle 24. MUF was calculated between Baghdad and 30 receiving stations for each month and hour. The effect of solar activity on MUF was more intense in 2000 compared to 2010, likely due to higher sunspot numbers in 2000.
Performance Analysis of New Light Weight Cryptographic AlgorithmsIOSR Journals
This document analyzes the performance of two new lightweight cryptographic algorithms: Hummingbird-2 and PRESENT. It finds that both algorithms provide adequate security against common attacks like differential and linear cryptanalysis. Hardware implementations of PRESENT require less area than Hummingbird-2 but consume more power. FPGA implementations show that PRESENT is more efficient on low-cost FPGAs, with higher throughput. However, analysis finds that Hummingbird-2 is generally better suited than PRESENT for resource-constrained devices due to its lower power consumption and higher throughput.
An Adaptive Masker for the Differential Evolution AlgorithmIOSR Journals
The document proposes an adaptive masker technique for the differential evolution algorithm to perform automatic fuzzy clustering. The adaptive masker aims to guide the search process towards the optimal clustering solution by dividing the mask matrix into three zones - a best masks zone, a global best influence zone where the number of clusters is a function of the best fitness, and a random zone. Experimental results on a remote sensing dataset show the proposed adaptive masker differential evolution algorithm performs better than other fuzzy clustering algorithms like iterative fuzzy c-means, improved differential evolution, and variable length genetic algorithm based fuzzy clustering in automatically detecting the optimal number of clusters.
The document discusses technologies the author learned about and used in creating a music magazine media product. These included slide presentation software SlideShare, photo editing software Photoshop and InDesign, web browsers, scanners, and multimedia software like QuickTime and Prezi. The author found these technologies enormously helpful for tasks like taking photos, editing images, layout, and hosting content online. Overall, the technologies helped the author create a higher quality, more professional media product than would have been possible without them.
Substitution Error Analysis for Improving the Word Accuracy in Telugu Langua...IOSR Journals
This document discusses substitution error analysis to improve word accuracy in an automatic speech recognition system for the Telugu language. It analyzes the performance of an ASR system using two different lexical models - one based on a stress-timed language (CMU) and the other a handcrafted lexicon for syllable-timed Telugu. The effect of gender, accents, and pronunciation variants on substitution errors is studied. Confusion matrices of vowels and consonants show the most common phoneme substitutions for each case. The Telugu-based lexicon improves word accuracy by 20-30% over the CMU-based system.
CPCRT: Crosslayered and Power Conserved Routing Topology for congestion Cont...IOSR Journals
The document describes a proposed Crosslayered and Power Conserved Routing Topology (CPCRT) for congestion control in mobile ad hoc networks. The CPCRT aims to improve transmission performance by distinguishing between packet loss due to link failure versus other causes, while also conserving power used for packet transmission. It builds upon an earlier Crosslayered Routing Topology (CRT) approach by incorporating power conservation. The CPCRT is intended to identify the root cause of packet loss, avoid unnecessary congestion handling from link failures, allow congestion handling at specific high-traffic nodes rather than all nodes, and optimize resource and power usage for packet routing in mobile ad hoc networks.
IOSR Journal of Humanities and Social Science is an International Journal edited by International Organization of Scientific Research (IOSR).The Journal provides a common forum where all aspects of humanities and social sciences are presented. IOSR-JHSS publishes original papers, review papers, conceptual framework, analytical and simulation models, case studies, empirical research, technical notes etc.
IOSR Journal of Business and Management (IOSR-JBM) is an open access international journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
ANALYSIS OF WATERMARKING TECHNIQUES FOR MEDICAL IMAGES PRESERVING ROI cscpconf
The document discusses watermarking techniques for medical images to preserve the region of interest (ROI) during transmission. It first provides background on the need for security in sharing medical images over networks. It then summarizes various techniques for segmenting the ROI from medical images, including thresholding, clustering, and edge detection methods applied to MRI and CT scans. The goal of the watermarking is to apply marks only to the region outside the ROI (RONI) to authenticate images without affecting diagnosis.
Survey on Segmentation Techniques for Spinal Cord ImagesIIRindia
Medical imaging is a technique which is used to expose the interior part of the body, to diagnose the diseases and to treat them as well. Different modalities are used to process the medical images. It helps the human specialists to make diagnosis ailments. In this paper, we surveyed segmentation on the spinal cord images using different techniques such as Data mining, Support vector machine, Neural Networks and Genetic Algorithm which are applied to find the disorders and syndromes affected in the spinal cord system. As a result, we have gained knowledge in an identified disarrays and ailments affected in lumbar vertebra, thoracolumbar vertebra and spinal canal. Finally how the Disc Similarity Index values are generated in each method is also analysed.
The document discusses image processing techniques for measuring dimensions from images. It proposes using image processing to determine lengths, diameters, splines, and caliper measurements by acquiring an image, smoothing it, segmenting it, and applying the Euclidean algorithm to find exact measurements in pixels. The approach could provide more accurate measurements than physical scales or tapes by marking individual pixel endpoints rather than human-visible lengths.
Computer vision is the science and technology of machines that see. It uses theories and models from fields like physics, neurobiology, and signal processing to build artificial systems that can obtain information from images. Typical applications of computer vision include industrial inspection, medical imaging, autonomous vehicles, and visual surveillance. Common computer vision tasks involve recognition, motion analysis, scene reconstruction, and image restoration.
Analysis Of Medical Image Processing And Its Application In HealthcarePedro Craggett
This document summarizes a research paper that analyzes medical image processing and its applications in healthcare. It discusses how medical image processing is an emerging field that can help with medical diagnosis. The paper focuses on detecting and extracting tumors from MRI scans of the brain using MATLAB. It describes preprocessing MRI images, performing segmentation, removing noise, and applying morphological operations. The goal is to accurately detect and extract tumor cells to help physicians with diagnosis. The techniques discussed include filtering, enhancement, and classification of features to analyze abnormal cells in MRI images.
Preprocessing Techniques for Image Mining on Biopsy ImagesIJERA Editor
This document discusses preprocessing techniques for image mining on biopsy images. It begins with an introduction to biomedical imaging and image mining. The key steps in image mining are described as image retrieval, preprocessing, feature extraction, data mining, and interpretation. Various preprocessing techniques are then evaluated on biopsy images, including interpolation, thresholding, and segmentation. Bicubic interpolation and Otsu thresholding produced good results for enhancing renal biopsy images. Overall, the document evaluates different preprocessing methods and their effects on biopsy images to help extract meaningful features for disease detection through image mining.
A Review on Brain Disorder Segmentation in MR ImagesIJMER
This document reviews various methods for automatically detecting brain tumors from MRI scans using computer-aided systems. It summarizes segmentation and classification approaches that have been used, including thresholding, region growing, genetic algorithms, clustering, and neural networks. The most common techniques are thresholding, region-based segmentation, and support vector machines or neural networks for classification. While these methods have achieved some success, challenges remain in developing systems that can accurately classify tumor types with high performance on diverse datasets. Future work may explore combining discrete and continuous segmentation approaches to improve computational efficiency and detection accuracy.
This document proposes a method for detecting brain tumors from MRI images using binary image processing and k-means clustering. MRI images are first converted to binary images using morphological filtering. This allows for more efficient hardware implementation of image processing operations like dilation and erosion. The binary images then undergo k-means clustering to segment and detect the tumor region. Simulation results show the tumor was successfully detected in binary images processed with morphological filtering and k-means clustering. The proposed method aims to reduce computational complexity and hardware requirements for brain tumor detection compared to existing methods.
AN EFFECTIVE AND EFFICIENT FEATURE SELECTION METHOD FOR LUNG CANCER DETECTIONijcsit
This document summarizes a research paper on an effective and efficient feature selection method for lung cancer detection. It discusses how feature selection can reduce the number of features in medical image analysis to extract the most important features for accurate image recognition and classification. The proposed method involves extracting the lung region from CT scans, segmenting the lung tissue, analyzing segments to extract diagnostic features, and applying classification rules to determine if cancer is present or not. Feature selection is shown to improve the performance of automated computer-aided diagnosis systems for early detection of lung cancer.
Important Aspects of Digital Pathology- A Focus on Whole Slide Imaging/Tissue...The Lifesciences Magazine
Applications of Digital Pathology, WSI, and Tissue Image Analysis: 1. Clinical Diagnostics 2. Medical Education 3. Research and Drug Development 4. Telepathology
This document reviews various automated techniques that have been developed for brain tumor detection. It summarizes research done by several researchers on methods like sequential floating forward selection, color coding schemes using brain atlases, neural networks, region growing segmentation combined with area calculation, symmetry analysis of tumor areas in MRI images, and combining clustering and classification algorithms. The paper concludes that image segmentation plays an important role in medical applications like tumor diagnosis and that more robust techniques are needed for high accuracy and reliability.
Lung Cancer Detection using Machine Learningijtsrd
Modern three dimensional 3 D medical imaging offers the potential and promise for major advances in science and medicine as higher fidelity images are produced. Due to advances in computer aided diagnosis and continuous progress in the field of computerized medical image visualization, there is need to develop one of the most important fields within scientific imaging. From the early basis report on cancer patients it has been seen that a greater number of people die of lung cancer than from other cancers such as colon, breast and prostate cancers combined. Lung cancer are related to smoking or secondhand smoke , or less often to exposure to radon or other environmental factors that’s why this can be prevented. But still it is not yet clear if these cancers can be prevented or not. In this research work, approach of segmentation, feature extraction and Convolution Neural Network CNN will be applied for locating, characterizing cancer portion. Harpreet Singh | Er. Ravneet Kaur | "Lung Cancer Detection using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33659.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-architecture/33659/lung-cancer-detection-using-machine-learning/harpreet-singh
A Review Paper On Brain Tumor Segmentation And DetectionScott Faria
This document summarizes a review paper on brain tumor segmentation and detection techniques. It discusses how MRI images are useful for studying brain tumors and outlines some common segmentation and detection methods like fuzzy transforms and morphological operations. The paper reviews several other papers on topics like the WHO brain tumor classification, abnormal MRI image segmentation using fuzzy clustering, neural networks for brain tumor detection, and a watershed-based method for color brain MRI segmentation. It concludes that automatic detection methods can achieve high accuracy in detecting and treating brain tumors.
Texture Analysis As An Aid In CAD And Computational Logiciosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document discusses the use of texture analysis in medical image analysis and computer-aided diagnosis (CAD) systems. It begins by providing background on texture analysis and its role in extracting features from medical images that can help with diagnosis. The document then discusses how texture analysis is used as a preprocessing step in CAD systems, where extracted texture features are fed into machine learning algorithms to perform diagnostic tasks. It also addresses some challenges with texture analysis and its implementation in CAD systems, noting further development and testing is still needed. Overall, the summary discusses how texture analysis opens new opportunities for CAD in radiology by automating the feature extraction process.
This document presents a method for detecting cancer in Pap smear cytological images using bag of texture features. The method involves segmenting the nucleus region from the images, extracting texture features from blocks within the nucleus region, clustering the features to build a visual dictionary, and representing each image as a histogram of visual words present. The histograms are then used to retrieve similar images from a database using histogram intersection as the distance measure. Experiments were conducted using different block sizes and number of clusters, achieving up to 90% accuracy in identifying cancerous versus normal cells.
Detection of Cancer in Pap smear Cytological Images Using Bag of Texture Feat...IOSR Journals
This document presents a method for detecting cancer in Pap smear cytological images using bag of texture features. The method involves segmenting the nucleus region from the images, extracting texture features from blocks of the nucleus region, clustering the features to build a visual dictionary, and representing each image as a histogram of visual words present. The histograms are then used to retrieve similar images from a database using histogram intersection as the distance measure. Experiments were conducted with different block sizes and number of clusters, achieving up to 90% accuracy in identifying cancerous versus normal cells.
A review deep learning for medical image segmentation using multi modality fu...Aykut DİKER
This paper reviews deep learning approaches for medical image segmentation using multi-modality fusion. It finds that the number of papers on this topic has increased significantly in recent years, as deep learning methods have achieved superior performance over traditional methods. The paper categorizes fusion strategies as early fusion, where modalities are combined before network processing, and late fusion, where each modality is processed separately before fusion. While early fusion is simpler, late fusion can achieve more accurate results by learning complex relationships between modalities. Overall, the paper aims to provide an overview of deep learning fusion methods for multi-modal medical image segmentation.
Implementation of Lower Leg Bone Fracture Detection from X Ray Imagesijtsrd
A methodology and various techniques are presented for development of fracture detection system using digital image processing. This paper presents an implementation of bone fracture detection using medical X ray image. The goal of this paper is to generate a quick and diagnosis can save time, effort and cost as well as reduce errors. The main objective of this research is to classify the lower leg bone fracture using X ray image because this type of bone are the most commonly occur in bone. This paper classifies the two types of fracture Non fracture and fracture or Transverse fracture by using SVM classifier. The proposed system has basically five steps, namely, image acquisition, image pre processing, image segmentation, feature extraction and image classification. San Myint | Khaing Thinzar "Implementation of Lower Leg Bone Fracture Detection from X-Ray Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27957.pdfPaper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/27957/implementation-of-lower-leg-bone-fracture-detection-from-x-ray-images/san-myint
IRJET- Segmentation and Visualization in Medical Image Processing: A ReviewIRJET Journal
This document provides a review of image processing, segmentation, and visualization techniques in medical imaging. It discusses how image processing aims to extract information from medical images for purposes like improving human interpretation, compressing data for storage, and enabling object detection. Segmentation involves partitioning an image into meaningful regions, which is important for feature extraction and image analysis. Visualization plays key roles in understanding and communicating medical image data through scientific visualization, data visualization, and interaction techniques. The document outlines various segmentation and visualization methods used in medical image analysis.
Similar to A Tailored Anti-Forensic Approach for Bitmap Compression in Medical Images (20)
This document provides a technical review of secure banking using RSA and AES encryption methodologies. It discusses how RSA and AES are commonly used encryption standards for secure data transmission between ATMs and bank servers. The document first provides background on ATM security measures and risks of attacks. It then reviews related work analyzing encryption techniques. The document proposes using a one-time password in addition to a PIN for ATM authentication. It concludes that implementing encryption standards like RSA and AES can make transactions more secure and build trust in online banking.
This document analyzes the performance of various modulation schemes for achieving energy efficient communication over fading channels in wireless sensor networks. It finds that for long transmission distances, low-order modulations like BPSK are optimal due to their lower SNR requirements. However, as transmission distance decreases, higher-order modulations like 16-QAM and 64-QAM become more optimal since they can transmit more bits per symbol, outweighing their higher SNR needs. Simulations show lifetime extensions up to 550% are possible in short-range networks by using higher-order modulations instead of just BPSK. The optimal modulation depends on transmission distance and balancing the energy used by electronic components versus power amplifiers.
This document provides a review of mobility management techniques in vehicular ad hoc networks (VANETs). It discusses three modes of communication in VANETs: vehicle-to-infrastructure (V2I), vehicle-to-vehicle (V2V), and hybrid vehicle (HV) communication. For each communication mode, different mobility management schemes are required due to their unique characteristics. The document also discusses mobility management challenges in VANETs and outlines some open research issues in improving mobility management for seamless communication in these dynamic networks.
This document provides a review of different techniques for segmenting brain MRI images to detect tumors. It compares the K-means and Fuzzy C-means clustering algorithms. K-means is an exclusive clustering algorithm that groups data points into distinct clusters, while Fuzzy C-means is an overlapping clustering algorithm that allows data points to belong to multiple clusters. The document finds that Fuzzy C-means requires more time for brain tumor detection compared to other methods like hierarchical clustering or K-means. It also reviews related work applying these clustering algorithms to segment brain MRI images.
1) The document simulates and compares the performance of AODV and DSDV routing protocols in a mobile ad hoc network under three conditions: when users are fixed, when users move towards the base station, and when users move away from the base station.
2) The results show that both protocols have higher packet delivery and lower packet loss when users are either fixed or moving towards the base station, since signal strength is better in those scenarios. Performance degrades when users move away from the base station due to weaker signals.
3) AODV generally has better performance than DSDV, with higher throughput and packet delivery rates observed across the different user mobility conditions.
This document describes the design and implementation of 4-bit QPSK and 256-bit QAM modulation techniques using MATLAB. It compares the two techniques based on SNR, BER, and efficiency. The key steps of implementing each technique in MATLAB are outlined, including generating random bits, modulation, adding noise, and measuring BER. Simulation results show scatter plots and eye diagrams of the modulated signals. A table compares the results, showing that 256-bit QAM provides better performance than 4-bit QPSK. The document concludes that QAM modulation is more effective for digital transmission systems.
The document proposes a hybrid technique using Anisotropic Scale Invariant Feature Transform (A-SIFT) and Robust Ensemble Support Vector Machine (RESVM) to accurately identify faces in images. A-SIFT improves upon traditional SIFT by applying anisotropic scaling to extract richer directional keypoints. Keypoints are processed with RESVM and hypothesis testing to increase accuracy above 95% by repeatedly reprocessing images until the threshold is met. The technique was tested on similar and different facial images and achieved better results than SIFT in retrieval time and reduced keypoints.
This document studies the effects of dielectric superstrate thickness on microstrip patch antenna parameters. Three types of probes-fed patch antennas (rectangular, circular, and square) were designed to operate at 2.4 GHz using Arlondiclad 880 substrate. The antennas were tested with and without an Arlondiclad 880 superstrate of varying thicknesses. It was found that adding a superstrate slightly degraded performance by lowering the resonant frequency and increasing return loss and VSWR, while decreasing bandwidth and gain. Specifically, increasing the superstrate thickness or dielectric constant resulted in greater changes to the antenna parameters.
This document describes a wireless environment monitoring system that utilizes soil energy as a sustainable power source for wireless sensors. The system uses a microbial fuel cell to generate electricity from the microbial activity in soil. Two microbial fuel cells were created using different soil types and various additives to produce different current and voltage outputs. An electronic circuit was designed on a printed circuit board with components like a microcontroller and ZigBee transceiver. Sensors for temperature and humidity were connected to the circuit to monitor the environment wirelessly. The system provides a low-cost way to power remote sensors without needing battery replacement and avoids the high costs of wiring a power source.
1) The document proposes a model for a frequency tunable inverted-F antenna that uses ferrite material.
2) The resonant frequency of the antenna can be significantly shifted from 2.41GHz to 3.15GHz, a 31% shift, by increasing the static magnetic field placed on the ferrite material.
3) Altering the permeability of the ferrite allows tuning of the antenna's resonant frequency without changing the physical dimensions, providing flexibility to operate over a wide frequency range.
This document summarizes a research paper that presents a speech enhancement method using stationary wavelet transform. The method first classifies speech into voiced, unvoiced, and silence regions based on short-time energy. It then applies different thresholding techniques to the wavelet coefficients of each region - modified hard thresholding for voiced speech, semi-soft thresholding for unvoiced speech, and setting coefficients to zero for silence. Experimental results using speech from the TIMIT database corrupted with white Gaussian noise at various SNR levels show improved performance over other popular denoising methods.
This document reviews the design of an energy-optimized wireless sensor node that encrypts data for transmission. It discusses how sensing schemes that group nodes into clusters and transmit aggregated data can reduce energy consumption compared to individual node transmissions. The proposed node design calculates the minimum transmission power needed based on received signal strength and uses a periodic sleep/wake cycle to optimize energy when not sensing or transmitting. It aims to encrypt data at both the node and network level to further optimize energy usage for wireless communication.
This document discusses group consumption modes. It analyzes factors that impact group consumption, including external environmental factors like technological developments enabling new forms of online and offline interactions, as well as internal motivational factors at both the group and individual level. The document then proposes that group consumption modes can be divided into four types based on two dimensions: vertical (group relationship intensity) and horizontal (consumption action period). These four types are instrument-oriented, information-oriented, enjoyment-oriented, and relationship-oriented consumption modes. Finally, the document notes that consumption modes are dynamic and can evolve over time.
The document summarizes a study of different microstrip patch antenna configurations with slotted ground planes. Three antenna designs were proposed and their performance evaluated through simulation: a conventional square patch, an elliptical patch, and a star-shaped patch. All antennas were mounted on an FR4 substrate. The effects of adding different slot patterns to the ground plane on resonance frequency, bandwidth, gain and efficiency were analyzed parametrically. Key findings were that reshaping the patch and adding slots increased bandwidth and shifted resonance frequency. The elliptical and star patches in particular performed better than the conventional design. Three antenna configurations were selected for fabrication and measurement based on the simulations: a conventional patch with a slot under the patch, an elliptical patch with slots
1) The document describes a study conducted to improve call drop rates in a GSM network through RF optimization.
2) Drive testing was performed before and after optimization using TEMS software to record network parameters like RxLevel, RxQuality, and events.
3) Analysis found call drops were occurring due to issues like handover failures between sectors, interference from adjacent channels, and overshooting due to antenna tilt.
4) Corrective actions taken included defining neighbors between sectors, adjusting frequencies to reduce interference, and lowering the mechanical tilt of an antenna.
5) Post-optimization drive testing showed improvements in RxLevel, RxQuality, and a reduction in dropped calls.
This document describes the design of an intelligent autonomous wheeled robot that uses RF transmission for communication. The robot has two modes - automatic mode where it can make its own decisions, and user control mode where a user can control it remotely. It is designed using a microcontroller and can perform tasks like object recognition using computer vision and color detection in MATLAB, as well as wall painting using pneumatic systems. The robot's movement is controlled by DC motors and it uses sensors like ultrasonic sensors and gas sensors to navigate autonomously. RF transmission allows communication between the robot and a remote control unit. The overall aim is to develop a low-cost robotic system for industrial applications like material handling.
This document reviews cryptography techniques to secure the Ad-hoc On-Demand Distance Vector (AODV) routing protocol in mobile ad-hoc networks. It discusses various types of attacks on AODV like impersonation, denial of service, eavesdropping, black hole attacks, wormhole attacks, and Sybil attacks. It then proposes using the RC6 cryptography algorithm to secure AODV by encrypting data packets and detecting and removing malicious nodes launching black hole attacks. Simulation results show that after applying RC6, the packet delivery ratio and throughput of AODV increase while delay decreases, improving the security and performance of the network under attack.
The document describes a proposed modification to the conventional Booth multiplier that aims to increase its speed by applying concepts from Vedic mathematics. Specifically, it utilizes the Urdhva Tiryakbhyam formula to generate all partial products concurrently rather than sequentially. The proposed 8x8 bit multiplier was coded in VHDL, simulated, and found to have a path delay 44.35% lower than a conventional Booth multiplier, demonstrating its potential for higher speed.
This document discusses image deblurring techniques. It begins by introducing image restoration and focusing on image deblurring. It then discusses challenges with image deblurring being an ill-posed problem. It reviews existing approaches to screen image deconvolution including estimating point spread functions and iteratively estimating blur kernels and sharp images. The document also discusses handling spatially variant blur and summarizes the relationship between the proposed method and previous work for different blur types. It proposes using color filters in the aperture to exploit parallax cues for segmentation and blur estimation. Finally, it proposes moving the image sensor circularly during exposure to prevent high frequency attenuation from motion blur.
This document describes modeling an adaptive controller for an aircraft roll control system using PID, fuzzy-PID, and genetic algorithm. It begins by introducing the aircraft roll control system and motivation for developing an adaptive controller to minimize errors from noisy analog sensor signals. It then provides the mathematical model of aircraft roll dynamics and describes modeling the real-time flight control system in MATLAB/Simulink. The document evaluates PID, fuzzy-PID, and PID-GA (genetic algorithm) controllers for aircraft roll control and finds that the PID-GA controller delivers the best performance.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
A Tailored Anti-Forensic Approach for Bitmap Compression in Medical Images
1. IOSR Journal of Computer Engineering (IOSRJCE)
ISSN: 2278-0661 Volume 5, Issue 1 (Sep-Oct. 2012), PP 01-05
www.iosrjournals.org
www.iosrjournals.org 1 | Page
A Tailored Anti-Forensic Approach for Bitmap Compression in
Medical Images
Manimurugan.S1
, Athira B.Kaimal2
1,2
computer science and engineering, Karunya University,india
Abstract: Medical imaging is the technique and process used to create images of the human body for clinical
purposes or medical science.Image processing has now become a significant component in almost all the areas.
But storing medical images in a safe and sound way has become very complicated. Processing of such images
should be carried out without knowledge of past processing on that image. Even though many image tampering
detection techniques are available, the number of image forgeries is increasing. In this paper, a new approach
is designed to prevent the medical image compression history. Then it also explains how this can be used to
perform unnoticeable forgeries on the medical images. It can be done by the estimation, examination and
alteration in the transform coefficients of image. The existing methods for identification of compression history
are JPEG detection and Quantizer estimation. The JPEG detection is used to find whether the image has been
previously compressed. But the proposed method indicates that proper addition of noise to an image’s
transform coefficients can adequately eliminate quantization artifacts which act as indicators of JPEG
compression. Using the proposed technique the modified image will appear to have never been compressed.
Therefore this technique can be used to cover the history of operations performed on the image and there by
rendering several forms of image tampering.
Keywords: JPEG compression, Image history, Image coefficients, Digital forensics, Anti-forensics, medical
imaging
I. Introduction
Medical imaging is the technique and process used to create images of the human body (or parts and
function thereof) for clinical purposes (medical procedures seeking to reveal, diagnose, or examine disease) or
medical science (including the study of normal anatomy and physiology). Although imaging of
removed organs and tissues can be performed for medical reasons, such procedures are not usually referred to as
medical imaging, but rather are a part of pathology. Measurement and recording techniques which are not
primarily designed to produce images, such as electroencephalography (EEG),electrocardiography (EKG),and
others, but which produce data susceptible to be represented as maps (i.e., containing positional information),
can be seen as forms of medical imaging.
Up until 2010, 5 billion medical imaging studies had been conducted worldwide. Radiation exposure
from medical imaging in 2006 made up about 50% of total ionizing radiation exposure in the United States. In
the clinical context, "invisible light" medical imaging is generally equated to radiology or "clinical imaging" and
the medical practitioner responsible for interpreting (and sometimes acquiring) the images is a radiologist.
"Visible light" medical imaging involves digital video or still pictures that can be seen without special
equipment. Dermatology and wound care are two modalities that utilize visible light imagery.
Diagnosticradiography designates the technical aspects of medical imaging and in particular the acquisition of
medical images. The radiographer or radiologic technologist is usually responsible for acquiring medical
images of diagnostic quality, although some radiological interventions are performed byradiologists. While
radiology is an evaluation of anatomy, nuclear medicine provides functional assessment.As a field of scientific
investigation, medical imaging constitutes a sub-discipline of biomedical engineering, medical
physics or medicinedepending on the context: Research and development in the area of instrumentation, image
acquisition (e.g. radiography), modeling and quantification are usually the preserve of biomedical
engineering, medical physics, and computer science; Research into the application and interpretation of medical
images is usually the preserve of radiology and the medical sub-discipline relevant to medical condition or area
of medical science(neuroscience, cardiology, psychiatry, psychology, etc.) under investigation. Many of the
techniques developed for medical imaging also havescientific and industrial applications.
Medical imaging is often perceived to designate the set of techniques that noninvasively produce
images of the internal aspect of the body. In this restricted sense, medical imaging can be seen as the solution
of mathematical inverse problems. This means that cause (the properties of living tissue) is inferred from effect
(the observed signal). In the case of ultrasonography the probe consists of ultrasonic pressure waves and echoes
inside the tissue show the internal structure. In the case of projection radiography, the probe is X-
ray radiation which is absorbed at different rates in different tissue types such as bone, muscle and fat.[27]
2. A Tailored Anti-Forensic Approach For Bitmap Compression In Medical Images
www.iosrjournals.org 2 | Page
In some situations, medical images are processed as bitmaps without any information of former
processing. It typically happens when image data is used as a bitmap without other information. The image
may have been already processed and compressed. But they may not be visually detectable. The images that are
usually stored as raster as they contain so much complex information that trying to store them as vector would
be unreasonably complex. If one wants to ensure that image is rendered it is enviable to realize the artifacts the
image might have, i.e., it is desirable to know a bit of the image’s “history.” Techniques are available to detect
manipulations of bitmap images and these make use of the transformation and other coefficient of images
[1][21][23].. It will help to find the prior processing informations.But if a forger with good knowledge in the
image processing and signal processing area can hide the evidence of compressions and other tampering. Since
images have become an important part of visual communication it is important to examine how much we can
trust on the available detection techniques and what all are the weaknesses .To examine the efficiency and to
prevent the manipulations of raster bitmap images many techniques are developed by researchers. These
techniques are designed to determine a bitmap images compression history. When the image processing units
inherit images in raster bitmap format the processing is to be carried without knowledge of past operations that
may compromise image quality (e.g., compression). To carry further processing, it is useful to not only know
whether the image has been previously JPEG compressed, but to learn what quantization table was used.
Consider the case, if one wants to remove JPEG artifacts or for JPEG re-compression, the existing techniques
show it can be detected through JPEG detection and Quantizer estimation [1]. To prevent the image forgeries
and to detect those researchers have developed a variety of techniques. They states that using the available
techniques such as finding blocking signature[1], estimation of quantization table etc, we can find the evidence
of JPEG compression [7]and thereby we can identify image forgeries as well as localized mismatches in JPEG
block[4][5].
The extensive availability of photo editing software has made it easy to create visually believable
digital image forgeries. To deal with this problem, there has been much recent work in the field of digital image
forensics. There has been little work, however, in the field of anti-forensics, which seeks to develop a set of
techniques designed to fool current forensic methodologies[22].JPEG compression history of an image can be
used to provide evidence of image manipulation[24], deliver information about the camera used to produce an
image, and discover forged areas within a picture [2].
The proper addition of noise to an image’s discrete cosine transform coefficients can sufficiently
remove quantization artifacts which act as indicators of JPEG compression while introducing an acceptable
level of distortion [3][12][18]. Though many existing JPEG detection techniques are capable of detecting a
variety of standard bitmap image manipulations, compression histories etc., they do not account for the
possibility that new techniques may be designed and used to hide image manipulation evidences. This is
particularly important because it calls into question the validity of results indicating the absence of image
tampering. It may be possible for an image forger familiar with signal processing to secretly develop new
techniques and use them to create undetectable compression and other image forgeries. As a result, several
existing techniques may contain unknown vulnerabilities [14][15].The researchers believe that processing in
raster bitmap images will reduce the quality and it can be used as visually identifiable evidence of tampering.
But we can develop new techniques which are capable of fooling existing detection methods and capable of
improving image quality [3]. Therefore they cannot find any evidence of compression as well as tampering in
images[16][17].
II. Proposed Method
To the best of knowledge the prior work for identifying bitmap compression history is JPEG detection
and quantization table estimation [1][8].In this paper a set of techniques capable of hiding the compression
history and evidences of image manipulations are presented. Since most of the techniques involve analyzing the
transform coefficients for the variations and blocking artifacts, this paper propose a new method for removing
the detectable traces from images.[19][20] The proposed algorithm can be used to fool most of the existing
techniques created for JPEG detection to identify bitmap image compression history. When images undergoes
through JPEG compression it will leave some quantization coefficients as evidence [1] [6]. In this paper these
are discussed first and then a method is proposed for hiding compression history in the bitmap images
If one want to ensure that the image is rendered, it is desirable to understand the artifacts that images
have. It is also desirable to know a bit of image’s history. Existing methods says that we can do this by detecting
whether the image has ever been compressed using JPEG standard [1]. In this paper a feasible method for
applying anti forensic techniques to hide the JPEG detection for identifying compression history is used. The
proposed method can be used to hide the evidence of image compression by removing DCT coefficient
fingerprints and by removing blocking artifacts of the image [2][8].The first step in the identification of the
image’s compression history is to identify whether the image has been compressed before or not. But the
tailored anti forensic method can show an already compressed image as a never compressed image. Therefore it
will not give any evidence of compression. It assumes that if there is no compression the pixel Differences
3. A Tailored Anti-Forensic Approach For Bitmap Compression In Medical Images
www.iosrjournals.org 3 | Page
across blocks should be similar to those within blocks. We find the differences using DCT coefficients. Let X
represents the blocks of image. For applying our method we need to calculate the coefficients of each block
before and after compression. First the image is divided into N number of blocks. For each block X (i, j) we
compute the coefficients of blocks and that of pixels in each block. Consider two blocks. Let X1 be the first
block and X2 be the second one.
X1(i,j)={a1,a2,a3,a4} (1)
X2(i,j)={b1,b2,b3,b4} (2)
where {a1,a2,a3,a4} and {b1,b2,b3,b4} are following two set of pixel values The first set represents the pixels
inside block X1 and second represents the pixels inside block X2.We have to find the DCT coefficients of these
two blocks and given pixels before and after compression. Let D1 represents the set of coefficients before
performing compression and let D1’ be the set of transform coefficients after transformation or compression.
Find the difference between D1 and D1’which is represented as T.
n
nDnDT )()(
'
11
(3)
When someone try to detect the history of compression this T value is used as the evidence since it
shows the difference in coefficient values. Therefore if we are able to hide this difference means we can hide the
history of compression and transformations. Using the proposed method we can do this. It is done by adding
some noise called tailored dither to the images transform coefficients so that the transform coefficients will
match the estimated one. After adding this noise we apply some quality improvement techniques so that the
images visual quality of the image will not be affected. Then compression is performed. The final result will be
a compressed or forged image with undetectable history of compression and tampering. The proposed technique
is explained in the following algorithm
III. Result And Discussion
The proposed approach can be used to hide the compression history and to remove JPEG blocking
artifacts without affecting the visual quality. For this the tailored anti-forensic approach is applied to five
images. Figure 1 is one of them. It shows the steps to be performed to implement the new technique. Fig1.1
show the original input Lena image and figure 1shows the same image before and after applying tailored anti-
forensic technique. The image (a) represents the original image in bitmap format taken as input. First we analyze
the coefficients and find the values then image (b) represents the same image after performing some
manipulations. After that we are applying compression and analyze the values and find the difference. Using the
value the tailored noise is calculated and which is added to the compressed image so that the values will match
with the estimated one. Then we apply some quality improvement techniques to improve the quality. The image
(d) shows the final output, and there is no noticeable difference between the input image and the resulting
image. Similar way the same technique is applied to the other four images also and the results are obtained as
shown. It is clear from the images that by just viewing the images nobody can find out any difference.from the
original one. That is the images resulted from after applying the proposed technique contains no visual
indicators of modification and compression. Since those who want to detect the modifications in the image will
not have access to the original image the resulted image cannot be compared against the original one. The
modified image will appear as unaltered image.From the resulting images no one can find any difference in the
images. But we have to consider the case where forensic techniques are applied for detecting statistical values.
As we know the forensic experts can analyze the transform coefficients by comparing the histograms of images.
If any difference is found it will be declared as a forged one. Therefore this paper introduces a method to hide
the difference between statistical coefficients of histograms of original image and manipulated one. It is capable
of fooling forensic researchers. This is done by applying the algorithm explained in the section2 to the image
after modification or compression. Since some tailored noise value is added to the modified image to match it
with the estimated value there will not be much noticeable difference in the histogram coefficients. To examine
the efficiency of the proposed method results are shown in the following figures. The figure 3 shows the
histograms of DCT coefficients of uncompressed bitmap images and that of same images after compression and
after applying the tailored technique. Analysis of transform coefficients distribution value of the images yields
4. A Tailored Anti-Forensic Approach For Bitmap Compression In Medical Images
www.iosrjournals.org 4 | Page
similar results. To verify that the proposed technique can hide the traces of image manipulations the following
processing are done on the images.
Table 1. The accuracy of tailored anti-forensic technique on medical images
Input
image
Size of
image
before
processing
Size of
image
after
processing
PSNR Error
Rate
Correlation
Coefficient
1 43KB 43.1 KB 68.54 0.0091 0.0932
2 103KB 103.01KB 68.94 0.0083 0.998
3 30KB 30.002KB 68.99 0.0082 0.979
4 289KB 289.2 KB 70.20 0.0062 0.983
5 67KB 67.1 KB 69.32 0.0076 0.964
The medical images store in raster bitmap format are taken as inputs then they are converted in to gray
scale images then the coefficient values are identified then the image is compressed using different quality
factors. Then the traces of compression is removed by adding some tailored noise to the compressed image and
the resulting images are tested using existing detection methods. If no evidence of compression is present then
the image is considered as never compressed one. The proposed method is applied on five images and
summarized The values indicate that there is not much difference between the values of original images and
tampered images. Since we have added some noise to equalize the coefficients there is a slight difference in the
size of image but it is negligible.
Fig.2 (a)image before compression (b)JPEG compressed image (c)applying tailored method(c)modified
image after applying tailored method which appear as unaltered image (e)Original input image
Fig.3.(a)Histogram of original medical image(b)Histogram of modified medical image after applying
tailored method
5. A Tailored Anti-Forensic Approach For Bitmap Compression In Medical Images
www.iosrjournals.org 5 | Page
Ms.Athira B.Kaimal received the B.E. degree in computer science and engineering in
2011 from the Anna University, College SSCET.She is currently doing M.Tech Research
in the area of Image Processing at the department of Computer Science and Engineering,
Karunya Versity
IV. Conclusion
The contribution of this paper is a tailored anti-forensic technique which is capable of fooling forensic
algorithms used to detect compression details and other manipulations on medical images stored in raster format
images. Here a reliable method for hiding the compression history is presented. To do this first a generalized
frame work is created for identifying and removing traces from images transform coefficients. According to this
the traces of image manipulation can be removed by estimating the distribution of transform coefficients before
compression then adding some noise to the compressed image so that the modified image’s coefficient matches
the distribution estimated. It is based on the analysis of transform coefficients of images. As the forgeries in
medical images can cause unwanted problems in human life it is important to find such forgeries. This paper
proposes a new method which challenges existing forensic algorithms.
V. Acknowledgement
We would like to express our gratitude to all those who gave us the possibility to complete this paper.
References
[1]. Z. Fan and R. de Queiroz, “Identification of bitmap compression history:JPEG detection and quantizer estimation,”IEEE
Trans.Image Process,vol.12, no.2,pp.230–235,Feb. 2003.
[2]. M. Chen, J. Fridrich, M. Goljan, and J. Luká˘s, “Determining image origin and integrity using sensor noise,” IEEE Trans. Inf.
ForensicsSecurity, vol. 3, no. 1, pp. 74–90, Mar. 2008.
[3]. Mathew C.Stamm and K.J.Ray Liu,” Anti-Forensic of Digital Image Compression”, IEEE Transaction on Information forensics
And security, Vol.6,No.3,September2011.
[4]. S. Ye, Q. N. Sun, and E.-C. Chang, “Detecting digital image forgeries by measuring nconsistencies of blocking artifact,” in Proc.
IEEE Int.Conf. Multimedia Expo, 2007, pp. 12–15.
[5]. J. He, Z. Lin, L.Wang, and X. Tang, “Detecting doctored JPEG images via DCT coefficient analysis,” in Proc. Eur. Conf. Computer
Vision,May 2006, vol. 3593, pp. 423–435
[6]. W. S. Lin, S. K. Tjoa, H. V. Zhao, and K. J. R. Liu, “Digital image source coder forensics via intrinsic fingerprints,” IEEE Trans.
Inf.Forensics Security, vol. 4, no. 3, pp. 460–475, Sep. 2009.
[7]. W. Pennebaker and J. Mitchell, JPEG: Still Image Data Compression Standard. New York: Van Nostrand Reinhold, 1993.
[8]. M. C. Stamm, S. K. Tjoa, W. S. Lin, and K. J. R. Liu, “Undetectable image tampering through JPEG compression anti-forensics,”
in Proc.IEEE Int. Conf. Image Process., Sep. 2010, pp. 2109–2112.
[9]. M. Chen, J. Fridrich, M. Goljan, and J. Luká˘s, “Determining image origin and integrity using sensor noise,” IEEE Trans. Inf.
Forensics Security, vol. 3, no. 1, pp. 74–90, Mar. 2008.
[10]. J. Luká˘s and J. Fridrich, “Estimation of primary quantization matrix in double compressed JPEG images,” in Proc. Digital
Forensic Research Workshop, Aug. 2003, pp. 5–8.
[11]. Avcibas, S. Bayram, N.Memon, M. Ramkumar, and B. Sankur, “A classifier design for detecting image manipulations,” in Proc.
IEEE Int. Conf. Image Process., Oct. 2004, vol. 4, pp. 2645–2648.
[12]. M. C. Stamm and K. J. R. Liu, “Forensic detection of image manipulation using statistical intrinsic fingerprints,” IEEE Trans. Inf.
Forensics Security, vol. 5, no. 3, pp. 492–506, Sep. 2010.
[13]. W. Pennebaker and J. Mitchell, JPEG: Still Image Data Compression Standard. New York: Van Nostrand Reinhold, 1993.
[14]. R. Rosenholtz and A. Zakhor, “Iterative procedures for reduction of blocking effects in transform image coding,” IEEE Trans.
Circuits Syst.Video Technol., vol. 2, pp. 91–94, Mar. 1992.
[15]. Z. Fan and R. Eschbach, “JPEG decompression with reduced artifacts,” in Proc. IS&T/SPIE Symp. Electronic Imaging: Image and
Video Compression, San Jose, CA, Feb. 1994.
[16]. Z. Fan and F. Li, “Reducing artifacts in JPEG decompression by segmentation and smoothing,” in Proc. IEEE Int. Conf. Image
Processing,vol. II, 1996, pp. 17–20.
[17]. Luo, C.W. Chen, K. J. Parker, and T. S. Huang, “Artifact reduction in low bit rate DCT-based image compression,” IEEE Trans.
Image Processing, vol. 5, pp. 1363–1368, 1996.
[18]. Chou, M. Crouse, and K. Ramchandran, “A simple algorithm for removing blocking artifacts in block-transform coded images,”
IEEE Signal Processing Lett., vol. 5, pp. 33–35, Feb. 1998.
[19]. Sir M. Kendall and A. Stuart, The Advanced Theory of Statistics. New York: Macmillan, 1977, vol. 2. Independent JPEG Group
Library.. [Online]. Available:http://www.ijg.org.
[20]. Swaminathan,M.Wu, and K. J. R. Liu, “Digital image forensics via intrinsic fingerprints,” IEEE Trans. Inf. Forensics Security, vol.
3, no. 1, pp. 101–117, Mar. 2008.
[21]. Weiqi Luo, Jiwu Huang and Guoping Qiu,” JPEG Error Analysis and Its Applications to Digital
[22]. Image Forensics”, IEEE Trans. Inf. Forensics Security, vol. 5, no. 3, Sep. 2010.
[23]. http://sig.umd.edu/events/
[24]. http://www.scribd.com
[25]. http://www.docstoc.com/docs/108996696/Advances-in-Digital-Image-Processing-and-Information- Technology
Dr. S. Manimurugan completed his Bachelor’s Degree from Anna University and he
received his Master’s Degree from Karunya University. He was highly commended for his
work in Image Processing and Information Security, for which he was honored with a PhD
from Anna University