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.
MRI Image Segmentation Using Gradient Based Watershed Transform In Level Set ...IJERA Editor
Brain image classification is one of the utmost imperative parts of clinical investigative tools. Brain images
typically comprise noise, inhomogeneity and sometimes deviation. Therefore, precise segmentation of brain
images is a very challenging task. Nevertheless, the process of perfect segmentation of these images is very
important and crucial for a spot-on diagnosis by clinical tools. Also, intensity inhomogeneity often arises in realworld
images, which presents a substantial challenge in image segmentation. The most extensively used image
segmentation algorithms are region-based and usually rely on the homogeneousness of the image intensities in
the sections of interest, which often fail to afford precise segmentation results due to the intensity
inhomogeneity. This Research presents a more accurate segmentation using Gradient Based watershed
transform in level set method for a medical diagnosis system. Experimental results proved that our method
validating a much better rate of segmentation accuracy as compare to the traditional approaches, results are also
validated in terms of certain Measure properties of image regions like eccentricity, perimeter etc.
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.
A malignant tumor, also called brain cancer, grows rapidly and often invades or crowds healthy areas of the brain. Brain tumors can affect white matter fibers by either infiltrating or displacing the tissue. When the myelin sheath is damaged or disappears, the conduction of impulses along nerve fibers slows down or fails completely. Diffusion Tensor Imaging (DTI) is a relatively new imaging technique that can be used to evaluate white matter in the brain. DTI has diagnostic implications by being able to pinpoint areas where normal water flow is disrupted, providing valuable information about the location of specific lesions. Edema, infiltration and destruction of white matter reduces the anisotropic nature of the white matter. The paper aims to segment tumor from the healthy brain tissues in Diffusion Tensor brain tumor images using Fuzzy C-Means clustering.
MRI Image Segmentation Using Gradient Based Watershed Transform In Level Set ...IJERA Editor
Brain image classification is one of the utmost imperative parts of clinical investigative tools. Brain images
typically comprise noise, inhomogeneity and sometimes deviation. Therefore, precise segmentation of brain
images is a very challenging task. Nevertheless, the process of perfect segmentation of these images is very
important and crucial for a spot-on diagnosis by clinical tools. Also, intensity inhomogeneity often arises in realworld
images, which presents a substantial challenge in image segmentation. The most extensively used image
segmentation algorithms are region-based and usually rely on the homogeneousness of the image intensities in
the sections of interest, which often fail to afford precise segmentation results due to the intensity
inhomogeneity. This Research presents a more accurate segmentation using Gradient Based watershed
transform in level set method for a medical diagnosis system. Experimental results proved that our method
validating a much better rate of segmentation accuracy as compare to the traditional approaches, results are also
validated in terms of certain Measure properties of image regions like eccentricity, perimeter etc.
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.
A malignant tumor, also called brain cancer, grows rapidly and often invades or crowds healthy areas of the brain. Brain tumors can affect white matter fibers by either infiltrating or displacing the tissue. When the myelin sheath is damaged or disappears, the conduction of impulses along nerve fibers slows down or fails completely. Diffusion Tensor Imaging (DTI) is a relatively new imaging technique that can be used to evaluate white matter in the brain. DTI has diagnostic implications by being able to pinpoint areas where normal water flow is disrupted, providing valuable information about the location of specific lesions. Edema, infiltration and destruction of white matter reduces the anisotropic nature of the white matter. The paper aims to segment tumor from the healthy brain tissues in Diffusion Tensor brain tumor images using Fuzzy C-Means clustering.
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
Glioblastomas brain tumour segmentation based on convolutional neural network...IJECEIAES
Brain tumour segmentation can improve diagnostics efficiency, rise the prediction rate and treatment planning. This will help the doctors and experts in their work. Where many types of brain tumour may be classified easily, the gliomas tumour is challenging to be segmented because of the diffusion between the tumour and the surrounding edema. Another important challenge with this type of brain tumour is that the tumour may grow anywhere in the brain with different shape and size. Brain cancer presents one of the most famous diseases over the world, which encourage the researchers to find a high-throughput system for tumour detection and classification. Several approaches have been proposed to design automatic detection and classification systems. This paper presents an integrated framework to segment the gliomas brain tumour automatically using pixel clustering for the MRI images foreground and background and classify its type based on deep learning mechanism, which is the convolutional neural network. In this work, a novel segmentation and classification system is proposed to detect the tumour cells and classify the brain image if it is healthy or not. After collecting data for healthy and non-healthy brain images, satisfactory results are found and registered using computer vision approaches. This approach can be used as a part of a bigger diagnosis system for breast tumour detection and manipulation.
A N E XQUISITE A PPROACH FOR I MAGE C OMPRESSION T ECHNIQUE USING L OSS...ijcsitcejournal
The imminent evolution in the field of medical imaging, telehealth and teleradiology services has been on a
significant rise with a dire need for a proficient structure for the compression of a DICOM (Digital
Imaging and Communications
in Medicine) standard medical image obtained through various modalities,
with clinical relevance and digitized clinical data, and various other diagnostic phenomena and the
progressive transmission of such a medical image over varying bandwidths. The data
loss redundancy
during the process of compression is to be maintained below the alarming level, meaning it is to be under
scanner without the loss of data/information. In this paper we present an efficient time bound algorithm
that utilizes a process flow
wherein multiple ROI sectors as well as the Non
-
ROI sector of the DICOM
image are considered in the algorithmic machine and the compression is done based upon a hybrid
compression algorithm by LZW & SPIHT encoder & decoder machines. The paper provides a m
agnitude of
the overall compression ratio involved in thus compressing the DICOM standard image. It also provides a
brief description about the PSNR values obtained after suitably compressing the image. We analyze the
various encoder scenarios and have pro
jected a suitable hybrid lossless compression algorithm that helps
in the retrieval of the data/information related to the image.
AUTOMATED SEGMENTATION OF FLUORESCENT AND FUNDS IMAGES BASED ON RETINAL BLOOD...acijjournal
Measurements of retinal blood vessel morphology have been shown to be related to the risk of
cardiovascular diseases. The wrong identification of vessels may result in a large variation of these
measurements, leading to a wrong clinical diagnosis. In this paper, we address the problem of
automatically identifying true vessels as a post processing step to vascular structure segmentation. We
model the segmented vascular structure as a vessel segment graph and formulate the problem of identifying
vessels as one of finding the optimal forest in the graph given a set of constraints. We design a method to
solve this optimization problem and evaluate it on a large real-world dataset of 2,446 retinal images.
Experiment results are analyzed with respect to actual measurements of vessel morphology. The results
show that the proposed approach is able to achieve 98.9% pixel precision and 98.7% recall of the true
vessels for clean segmented retinal images, and remains robust even when the segmented image is noisy.
Intracerebral Hemorrhage (ICH): Understanding the CT imaging featuresPetteriTeikariPhD
Overview of CT basics and deep learning literature mostly focused on the analysis of ICH.
Intracerebral hemorrhage (ICH), also known as cerebral bleed, is a type of intracranial bleed that occurs within the brain tissue or ventricles. Intracerebral bleeds are the second most common cause of stroke, accounting for 10% of hospital admissions for stroke.
For spontaneous ICH seen on CT scan, the death rate (mortality) is 34–50% by 30 days after the insult,and half of the deaths occur in the first 2 days. Even though the majority of deaths occurs in the first days after ICH, survivors have a long term excess mortality of 27% compared to the general population.
Deep learning and computational steps roughly can be categorized to 1) Preprocessing, 2) Image Restoration (denoising, deblurring, inpainting, reconstruction), 3) Diffeomorphic registration for spatial normalization, 4) Hand-crafted radiomics and texture analysis, 5) Hemorrhage segmentation, among other relevant head CT issues
Alternative download link: https://www.dropbox.com/s/8l2h93cl2pmle4g/CT_hemorrhage.pdf?dl=0
A Review of Super Resolution and Tumor Detection Techniques in Medical Imagingijtsrd
Images with high resolution are desirable in many applications such as medical imaging, video surveillance, astronomy etc. In medical imaging, images are obtained for medical investigative purposes and for providing information about the anatomy, the physiologic and metabolic activities of the volume below the skin. Medical imaging is an important diagnosis instrument to determine the presence of certain diseases. Therefore increasing the image resolution should significantly improve the diagnosis ability for corrective treatment. Brain tumor detection is used for identifying the tumor present in the Brain. MRI images help the doctors for identifying the Brain tumor size and shape of the tumor. The purpose of this report to provide a survey of research related super resolution and tumor detection methods. Fathimath Safana C. K | Sherin Mary Kuriakose ""A Review of Super Resolution and Tumor Detection Techniques in Medical Imaging"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23525.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/23525/a-review-of-super-resolution-and-tumor-detection-techniques-in-medical-imaging/fathimath-safana-c-k
Segmentation and Classification of Brain MRI Images Using Improved Logismos-B...IJERA Editor
Automated reconstruction and diagnosis of brain MRI images is one of the most challenging problems in medical imaging. Accurate segmentation of MRI images is a key step in contouring during radiotherapy analysis. Computed tomography (CT) and Magnetic resonance (MR) imaging are the most widely used radiographic techniques in diagnosis and treatment planning. Segmentation techniques used for the brain Magnetic Resonance Imaging (MRI) is one of the methods used by the radiographer to detect any abnormality specifically in brain. The method also identifies important regions in brain such as white matter (WM), gray matter (GM) and cerebrospinal fluid spaces (CSF). These regions are significant for physician or radiographer to analyze and diagnose the disease. We propose a novel clustering algorithm, improved LOGISMOS-B to classify tissue regions based on probabilistic tissue classification, generalized gradient vector flows with cost and distance function. The LOGISMOS graph segmentation framework. Expand the framework to allow regionally-aware graph construction and segmentation.
A Novel Approach for Diabetic Retinopthy ClassificationIJERA Editor
Sustainable Diabetic Mellitus may lead to several complications towards patients. One of the complications is
diabetic retinopathy. Diabetic retinopathy is the type of complication towards the retinal and interferes with
patient’s sight. Medical examination toward patients with diabetic retinopathy is observed directly through
retinal images using fundus camera. Diabetic retinopathy is classified into four classes based on severity, which
are: normal, non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), and
macular edema (ME). The aim of this research is to develop a method which can be used to classify the level of
severity of diabetic retinopathy based on patient’s retinal images. Seven texture features were extracted from
retinal images using gray level co-occurence matrix three dimensional method (3D-GLCM). These features are
maximum probability, correlation, contrast, energy, homogeneity, and entropy; subsequently trained using
Levenberg-Marquardt Backpropagation Neural Network (LMBP). This study used 600 data of patient’s retinal
images, consist of 450 data retinal images for training and 150 data retinal images for testing. Based on the result
of this test, the method can classify the severity of diabetic retinopathy with sensitivity of 97.37%, specificity of
75% and accuracy of 91.67%
Digital imaging /certified fixed orthodontic courses by Indian dental academy Indian dental academy
The Indian Dental Academy is the Leader in continuing dental education , training dentists in all aspects of dentistry and offering a wide range of dental certified courses in different formats.
Indian dental academy provides dental crown & Bridge,rotary endodontics,fixed orthodontics,
Dental implants courses.for details pls visit www.indiandentalacademy.com ,or call
0091-9248678078
Faro An Interactive Interface For Remote Administration Of Clinical Tests Bas...Kalle
A challenging goal today is the use of computer networking and advanced
monitoring technologies to extend human intellectual capabilities in medical decision making. Modern commercial eye trackers
are used in many of research fields, but the improvement of eye tracking technology, in terms of precision on the eye movements capture, has led to consider the eye tracker as a tool for vision analysis, so that its application in medical research, e.g. in ophthalmology, cognitive psychology and in neuroscience has grown considerably. The improvements of the human eye tracker interface become more and more important to allow medical doctors to increase their diagnosis capacity, especially if the interface allows them to remotely administer the clinical tests more appropriate for the problem at hand. In this paper, we propose a client/server eye tracking system that provides an interactive system for monitoring patients eye movements depending on the clinical test administered by the medical doctors. The system supports the retrieval of the gaze information and provides statistics to both medical research and disease diagnosis.
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.
Brain Tumor Detection using CT scan Image by Image Processingijtsrd
Hydrocephalus or brain tumor is critical problem in the medical world and also milestone in medical industries because analysis the brain tumor is bigger issue. It’s very difficult to analysis the tumor available in brain. And it’s essential to evaluate pre operation as well as the post operation. Analysis the tumor in pre operation is quite easy compare to post operation because pre operation is straight forward problem. We have more advance technology to analysis but in cause of the post operation and analysis because after operation we can’t able to pressing on the brain due to distorted anatomy and subdural from brain and CSF so we used a CT scan Computational Tomographic for segmentation Of brain image in various dimension. So it’s quite easy to analysis the problem. We can also identify the spot of the damage accurately and it useful treatment by using some advance technology we can also detect whether cancer or normal tumor. So that it easy to medical world to treat further because in medical world analysis and spot the disease is a milestone. This process became easy, fast and efficient diagnosis. U. Indumathy | Mr. M. Anand "Brain Tumor Detection using CT scan Image by Image Processing" 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/ijtsrd33413.pdf Paper Url: https://www.ijtsrd.com/computer-science/other/33413/brain-tumor-detection-using-ct-scan-image-by-image-processing/u-indumathy
Recent advances in diagnosis and treatment planning1 /certified fixed orthod...Indian dental academy
The Indian Dental Academy is the Leader in continuing dental education , training dentists in all aspects of dentistry and offering a wide range of dental certified courses in different formats.
Indian dental academy provides dental crown & Bridge,rotary endodontics,fixed orthodontics,
Dental implants courses.for details pls visit www.indiandentalacademy.com ,or call
0091-9248678078
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.
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.
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
Glioblastomas brain tumour segmentation based on convolutional neural network...IJECEIAES
Brain tumour segmentation can improve diagnostics efficiency, rise the prediction rate and treatment planning. This will help the doctors and experts in their work. Where many types of brain tumour may be classified easily, the gliomas tumour is challenging to be segmented because of the diffusion between the tumour and the surrounding edema. Another important challenge with this type of brain tumour is that the tumour may grow anywhere in the brain with different shape and size. Brain cancer presents one of the most famous diseases over the world, which encourage the researchers to find a high-throughput system for tumour detection and classification. Several approaches have been proposed to design automatic detection and classification systems. This paper presents an integrated framework to segment the gliomas brain tumour automatically using pixel clustering for the MRI images foreground and background and classify its type based on deep learning mechanism, which is the convolutional neural network. In this work, a novel segmentation and classification system is proposed to detect the tumour cells and classify the brain image if it is healthy or not. After collecting data for healthy and non-healthy brain images, satisfactory results are found and registered using computer vision approaches. This approach can be used as a part of a bigger diagnosis system for breast tumour detection and manipulation.
A N E XQUISITE A PPROACH FOR I MAGE C OMPRESSION T ECHNIQUE USING L OSS...ijcsitcejournal
The imminent evolution in the field of medical imaging, telehealth and teleradiology services has been on a
significant rise with a dire need for a proficient structure for the compression of a DICOM (Digital
Imaging and Communications
in Medicine) standard medical image obtained through various modalities,
with clinical relevance and digitized clinical data, and various other diagnostic phenomena and the
progressive transmission of such a medical image over varying bandwidths. The data
loss redundancy
during the process of compression is to be maintained below the alarming level, meaning it is to be under
scanner without the loss of data/information. In this paper we present an efficient time bound algorithm
that utilizes a process flow
wherein multiple ROI sectors as well as the Non
-
ROI sector of the DICOM
image are considered in the algorithmic machine and the compression is done based upon a hybrid
compression algorithm by LZW & SPIHT encoder & decoder machines. The paper provides a m
agnitude of
the overall compression ratio involved in thus compressing the DICOM standard image. It also provides a
brief description about the PSNR values obtained after suitably compressing the image. We analyze the
various encoder scenarios and have pro
jected a suitable hybrid lossless compression algorithm that helps
in the retrieval of the data/information related to the image.
AUTOMATED SEGMENTATION OF FLUORESCENT AND FUNDS IMAGES BASED ON RETINAL BLOOD...acijjournal
Measurements of retinal blood vessel morphology have been shown to be related to the risk of
cardiovascular diseases. The wrong identification of vessels may result in a large variation of these
measurements, leading to a wrong clinical diagnosis. In this paper, we address the problem of
automatically identifying true vessels as a post processing step to vascular structure segmentation. We
model the segmented vascular structure as a vessel segment graph and formulate the problem of identifying
vessels as one of finding the optimal forest in the graph given a set of constraints. We design a method to
solve this optimization problem and evaluate it on a large real-world dataset of 2,446 retinal images.
Experiment results are analyzed with respect to actual measurements of vessel morphology. The results
show that the proposed approach is able to achieve 98.9% pixel precision and 98.7% recall of the true
vessels for clean segmented retinal images, and remains robust even when the segmented image is noisy.
Intracerebral Hemorrhage (ICH): Understanding the CT imaging featuresPetteriTeikariPhD
Overview of CT basics and deep learning literature mostly focused on the analysis of ICH.
Intracerebral hemorrhage (ICH), also known as cerebral bleed, is a type of intracranial bleed that occurs within the brain tissue or ventricles. Intracerebral bleeds are the second most common cause of stroke, accounting for 10% of hospital admissions for stroke.
For spontaneous ICH seen on CT scan, the death rate (mortality) is 34–50% by 30 days after the insult,and half of the deaths occur in the first 2 days. Even though the majority of deaths occurs in the first days after ICH, survivors have a long term excess mortality of 27% compared to the general population.
Deep learning and computational steps roughly can be categorized to 1) Preprocessing, 2) Image Restoration (denoising, deblurring, inpainting, reconstruction), 3) Diffeomorphic registration for spatial normalization, 4) Hand-crafted radiomics and texture analysis, 5) Hemorrhage segmentation, among other relevant head CT issues
Alternative download link: https://www.dropbox.com/s/8l2h93cl2pmle4g/CT_hemorrhage.pdf?dl=0
A Review of Super Resolution and Tumor Detection Techniques in Medical Imagingijtsrd
Images with high resolution are desirable in many applications such as medical imaging, video surveillance, astronomy etc. In medical imaging, images are obtained for medical investigative purposes and for providing information about the anatomy, the physiologic and metabolic activities of the volume below the skin. Medical imaging is an important diagnosis instrument to determine the presence of certain diseases. Therefore increasing the image resolution should significantly improve the diagnosis ability for corrective treatment. Brain tumor detection is used for identifying the tumor present in the Brain. MRI images help the doctors for identifying the Brain tumor size and shape of the tumor. The purpose of this report to provide a survey of research related super resolution and tumor detection methods. Fathimath Safana C. K | Sherin Mary Kuriakose ""A Review of Super Resolution and Tumor Detection Techniques in Medical Imaging"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23525.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/23525/a-review-of-super-resolution-and-tumor-detection-techniques-in-medical-imaging/fathimath-safana-c-k
Segmentation and Classification of Brain MRI Images Using Improved Logismos-B...IJERA Editor
Automated reconstruction and diagnosis of brain MRI images is one of the most challenging problems in medical imaging. Accurate segmentation of MRI images is a key step in contouring during radiotherapy analysis. Computed tomography (CT) and Magnetic resonance (MR) imaging are the most widely used radiographic techniques in diagnosis and treatment planning. Segmentation techniques used for the brain Magnetic Resonance Imaging (MRI) is one of the methods used by the radiographer to detect any abnormality specifically in brain. The method also identifies important regions in brain such as white matter (WM), gray matter (GM) and cerebrospinal fluid spaces (CSF). These regions are significant for physician or radiographer to analyze and diagnose the disease. We propose a novel clustering algorithm, improved LOGISMOS-B to classify tissue regions based on probabilistic tissue classification, generalized gradient vector flows with cost and distance function. The LOGISMOS graph segmentation framework. Expand the framework to allow regionally-aware graph construction and segmentation.
A Novel Approach for Diabetic Retinopthy ClassificationIJERA Editor
Sustainable Diabetic Mellitus may lead to several complications towards patients. One of the complications is
diabetic retinopathy. Diabetic retinopathy is the type of complication towards the retinal and interferes with
patient’s sight. Medical examination toward patients with diabetic retinopathy is observed directly through
retinal images using fundus camera. Diabetic retinopathy is classified into four classes based on severity, which
are: normal, non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), and
macular edema (ME). The aim of this research is to develop a method which can be used to classify the level of
severity of diabetic retinopathy based on patient’s retinal images. Seven texture features were extracted from
retinal images using gray level co-occurence matrix three dimensional method (3D-GLCM). These features are
maximum probability, correlation, contrast, energy, homogeneity, and entropy; subsequently trained using
Levenberg-Marquardt Backpropagation Neural Network (LMBP). This study used 600 data of patient’s retinal
images, consist of 450 data retinal images for training and 150 data retinal images for testing. Based on the result
of this test, the method can classify the severity of diabetic retinopathy with sensitivity of 97.37%, specificity of
75% and accuracy of 91.67%
Digital imaging /certified fixed orthodontic courses by Indian dental academy Indian dental academy
The Indian Dental Academy is the Leader in continuing dental education , training dentists in all aspects of dentistry and offering a wide range of dental certified courses in different formats.
Indian dental academy provides dental crown & Bridge,rotary endodontics,fixed orthodontics,
Dental implants courses.for details pls visit www.indiandentalacademy.com ,or call
0091-9248678078
Faro An Interactive Interface For Remote Administration Of Clinical Tests Bas...Kalle
A challenging goal today is the use of computer networking and advanced
monitoring technologies to extend human intellectual capabilities in medical decision making. Modern commercial eye trackers
are used in many of research fields, but the improvement of eye tracking technology, in terms of precision on the eye movements capture, has led to consider the eye tracker as a tool for vision analysis, so that its application in medical research, e.g. in ophthalmology, cognitive psychology and in neuroscience has grown considerably. The improvements of the human eye tracker interface become more and more important to allow medical doctors to increase their diagnosis capacity, especially if the interface allows them to remotely administer the clinical tests more appropriate for the problem at hand. In this paper, we propose a client/server eye tracking system that provides an interactive system for monitoring patients eye movements depending on the clinical test administered by the medical doctors. The system supports the retrieval of the gaze information and provides statistics to both medical research and disease diagnosis.
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.
Brain Tumor Detection using CT scan Image by Image Processingijtsrd
Hydrocephalus or brain tumor is critical problem in the medical world and also milestone in medical industries because analysis the brain tumor is bigger issue. It’s very difficult to analysis the tumor available in brain. And it’s essential to evaluate pre operation as well as the post operation. Analysis the tumor in pre operation is quite easy compare to post operation because pre operation is straight forward problem. We have more advance technology to analysis but in cause of the post operation and analysis because after operation we can’t able to pressing on the brain due to distorted anatomy and subdural from brain and CSF so we used a CT scan Computational Tomographic for segmentation Of brain image in various dimension. So it’s quite easy to analysis the problem. We can also identify the spot of the damage accurately and it useful treatment by using some advance technology we can also detect whether cancer or normal tumor. So that it easy to medical world to treat further because in medical world analysis and spot the disease is a milestone. This process became easy, fast and efficient diagnosis. U. Indumathy | Mr. M. Anand "Brain Tumor Detection using CT scan Image by Image Processing" 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/ijtsrd33413.pdf Paper Url: https://www.ijtsrd.com/computer-science/other/33413/brain-tumor-detection-using-ct-scan-image-by-image-processing/u-indumathy
Recent advances in diagnosis and treatment planning1 /certified fixed orthod...Indian dental academy
The Indian Dental Academy is the Leader in continuing dental education , training dentists in all aspects of dentistry and offering a wide range of dental certified courses in different formats.
Indian dental academy provides dental crown & Bridge,rotary endodontics,fixed orthodontics,
Dental implants courses.for details pls visit www.indiandentalacademy.com ,or call
0091-9248678078
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
From June to September 2013, Jim Anderson and Alice Ferris of GoalBusters Consulting embarked on a mission to thank someone every day for 100 days. The 100 Days of Gratitude was supposed to be a way to get us to blog more, but instead, it was an inspiring, emotional, touching, frustrating, occasionally dramatic, and, in the end, transforming experience.
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.
Small overview of the startups involved in healthcare artificial intelligence, the OCT market, investments, patent and IP issues and FDA regulation.
Alternative download link: https://dl.dropboxusercontent.com/u/6757026/slideShare/retinalAI_landscape.pdf
ANALYSIS OF WATERMARKING TECHNIQUES FOR MEDICAL IMAGES PRESERVING ROI cscpconf
Telemedicine is a well-known application, where enormous amount of medical data need to be securely transfer 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 paper discusses the available medical image watermarking methods for protecting and authenticating
medical data. The paper focuses on algorithms for application of watermarking 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.
Telemedicine; use of telecommunication and information technological services, which permits the
communication between the users with convenience and fidelity, as well transmitting medical, images and
health informatics data. Numerous image processing applications like Satellite Imaging, Medical Imaging
and Video has images with too large size or stream size, with a large amount of space or high bandwidth
for communication in its original form. Integrity of the transmitted medical images and the informatics
data, without any compromise in the data is an essential product of telecommunication and information
technology. A colossal need for an adequate compression methodology, in adoption for the compression of
medical images /data, to domicile for various metrics like high bandwidth, resolution factors, storage of the
images/data, the obligation to perpetuate the validity and precision of data for subsequent perceived
diagnosis transactions. This leverages exacting coercions on the restoration error. In this paper we survey
the literature related to the Image Processing Methodologies based on ROI technique/s for Digital Imaging
and Communication for Medicine (DICOM). A scrutiny as such persuades with the several congestions
related to prospective techniques of lossless compression, recommending for a better and a unique image
compression technique.
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A review on region of interest-based hybrid medical image compression algorithmsTELKOMNIKA JOURNAL
Digital medical images have become a vital resource that supports decision-making and treatment procedures in healthcare facilities. The medical image consumes large sizes of memory, and the size keeps on growth due to the trend of medical image technology. The technology of telemedicine encourages the medical practitioner to share the medical image to support knowledge sharing to diagnose and analyse the image. The healthcare system needs to ensure distributes the medical image accurately with zero loss of information, fast and secure. Image compression is beneficial in ensuring that achieve the goal of sharing this data. The region of interest-based hybrid medical compression algorithm plays the parts to reduce the image size and shorten the time of medical image compression process. Various studies have enhanced by combining numerous techniques to get an ideal result. This paper reviews the previous works conducted on a region of interest-based hybrid medical image compression algorithms.
Embedding and Extraction Techniques for Medical Images-Issues and Challenges csandit
New technologies in multimedia and communication fields have introduced new ways to transfer and save the medical image data through open networks, which has introduced new risks of inappropriate use of medical information. Medical images are highly sensitive hence secured transmission and reception of data is needed with minimal distortion. Medical image security plays an important role in the field of Telemedicine. Telemedicine has numerous applications in teleconsulting, teleradiology, telediagnosis, telesurgery and remote medical
education. Our work is to analyze about the different embedding techniques that can be used for embedding the personal and diagnosed details of a person within the medical images
without any visual discrepancy. Also to survey about the blind extraction algorithm utilizing genetic algorithm for optimization of the key parameters.
Overview of convolutional neural networks architectures for brain tumor segm...IJECEIAES
Due to the paramount importance of the medical field in the lives of people, researchers and experts exploited advancements in computer techniques to solve many diagnostic and analytical medical problems. Brain tumor diagnosis is one of the most important computational problems that has been studied and focused on. The brain tumor is determined by segmentation of brain images using many techniques based on magnetic resonance imaging (MRI). Brain tumor segmentation methods have been developed since a long time and are still evolving, but the current trend is to use deep convolutional neural networks (CNNs) due to its many breakthroughs and unprecedented results that have been achieved in various applications and their capacity to learn a hierarchy of progressively complicated characteristics from input without requiring manual feature extraction. Considering these unprecedented results, we present this paper as a brief review for main CNNs architecture types used in brain tumor segmentation. Specifically, we focus on researcher works that used the well-known brain tumor segmentation (BraTS) dataset.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
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Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
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In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
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Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Neuro-symbolic is not enough, we need neuro-*semantic*
Er36881887
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ISSN : 2248-9622, Vol. 3, Issue 6, Nov-Dec 2013, pp.881-887
RESEARCH ARTICLE
www.ijera.com
OPEN ACCESS
Retinal Image Compression In Favour Of Hasty Transmission
Using Region of Interest
E. Sivasankari1 D.Gowthami2 and Prof.R.Jayanthi3
1
(PG Scholar, Department of Electronics and Communication Engineering, Nandha College of Technology,
Erode-52)
2
(PG Scholar, Department of Electronics and Communication Engineering, Nandha College of Technology,
Erode-52)
3
(Assosiative Professor,Department of Electronics and Communication Engineering, Nandha College of
Technology, Erode-638052)
ABSTRACT
Teleophthalmology illustrated by transmission of retinal images and data between users is one of the promising
fields in medicine. Colossal bandwidth is essential for transmitting retinal images over the wireless network. So
the most essential aspiration of proposed system is to provide an efficient tool for defining to maximize
compression and reconstruct image portions lossless for high speed, efficient transmission and diminution in
storage space. This paper is proposed to investigate multiple compression techniques based on Region of
Interest (ROI). In the diagnosis of retinal images, the significant part is separated out from the rest of the image
using improved adaptive fuzzy C means algorithm and Integer multi wavelet transform is applied to enhance the
visual quality in significant part. The region of less significance are compressed using SPIHT algorithm and
finally modified embedded zero tree wavelet algorithm is applied which uses six symbols was applied whole
image then Huffman coding is applied to get the compressed image for transmission. The proposed algorithm
would give better quality, if the images used ROI compared to that of the other methods. The proposed
techniques can be evaluated for performance using Compression Ratio (CR), Peak Signal to Noise Ratio
(PSNR), Normalized Average Error (NAE), Average Difference (AD), Maximum Difference (MD), Mean
Square Error (MSE), Root Mean Square Error (RMSE), Signal to Noise Ratio (SNR), Normalized Cross
Correlation (NCC), Structural Content (SC), Encoding and Decoding time. The consequence scrutinized using
MATLAB and realized in hardware. As the conclusion, the inaccessible patients used a low cost access to
specialist’s eye checkups at crucial healthcare clinics, and at the same time, diminish unnecessary face-to-face
consultation at the hospital specialist’s center.
Keywords: Retinal Images, Image Compression, Region of Interest, Integer Multi wavelet Transform, SHIPT,
and Modified Embedded Zero Trees.
I.
Introduction
One of the most important organs of human
body is eye, which gives the sensation of vision
including color differentiation and perception of
depth due to the presence of rods and cones in the
retina. Definitely, the blindness result produces in
both physical and emotional disturbance for every
patient. This situation is most likely due to an acute
scarcity of ophthalmology specialists in many areas
and the distribution of ophthalmologists is remote
from uniform such that there are much more eye
disease patients in the rural regions, e.g., in India,
79% eye patients reside in rural areas. To resolve this
trouble, as a promising technology-based solution,
telemedicine, enables the doctor to discuss with the
patient remotely through video conferencing, share
data, and images the patient so as to reduce the
unnecessary referrals and travel cost.
Teleophthalmology is the branch of the
telemedicine. In conventional ophthalmology, most of
the analytic ophthalmic instrumentations are adapted
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to accumulate still and/or video cameras to acquire
images so that ophthalmologists create analytic
inferences. Retinal images are acquired by a
specialized camera called fundus camera. Mydriatic
and non-mydriatic fundus cameras are used for retinal
photography.Mydriatic camera requires dilation of
pupils. It provides good quality fundus images than
non mydriatic camera.
Teleophthalmology system will provide eye
consultation by delivering high-quality eye images
and videos over a public broadband network, so as to
utilize the use of communication technologies to give
ophthalmology services, share and optimize medical
expertise
locally,
as
well
as
globally.
Teleophthalmology was focused on particular eye
problem, such as diabetic retinopathy and macular
degeneration.
Diabetic retinopathy (DR) occurs in patients
suffering from diabetes, which causes damage to the
retina of the eye. This finally leads to total vision loss.
Diabetes is caused due to the body’s inability to store
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and make use of the sugar level in the blood. Diabetic
retinopathy causes damage to the blood vessels in the
retina, and this cause fluid to leak to leak into the
macula region of the retina causing it to enlarge and
leading to blurred vision. Diabetic retinopathy has
two types namely non proliferative and proliferative
type.
Age related macular degeneration (AMD)
occurs in the older group peoples, which affects the
macula and degeneration of capillaries in fundus and
in the central part of retina. This occurs due to the non
functionality of the Bruch’s membrane, which passes
the waste products and the nutrients to the retina from
the choroid. Fluid leaks out from the damaged vessels
and deposited at the center of macula, which results
blurring, obscuring or distorting vision. These liquid
deposits are called exudates.
Huge amount of image is produced in the
field of medical image, which can be stored in picture
archiving and communication system (PACS). So, it
is really hard for hospitals to manage the storing
facilities for the same. Moreover, such high data
demand for high end network especially for
transmitting the images over the network such as in
telemedicine. So compression is used and can be
categorized into two categories: lossless and lossy
compressions.
Lossless image compression is achieved if
original input image is recovered perfectly from
compressed data while lossy image compression
cannot regenerate original image data. Lossy image
compression, can maintain most details of original
image, useful for diagnosis. Precise image detail
preservation is not strictly required as an image’s
degraded portion is usually often not visible to
humans. But lossy image compression is not commonly used in clinical practice/diagnosis because
even with slight loss of data, it is possible for
physicians/radiologists to miss critical diagnostic
information that is needed for diagnosis of a patient.
Historically, medical image compression was investigated by researchers working in the image fields.
Consequently, technological growth in this field is a
by-product of progress in the more general field of
natural image. There is no golden rule: different
coding algorithms fit different types of images and
scenarios best. Depending on imaging modalities and
applications, some are better suited than others to
fulfill certain targets in terms of compression factor or
with respect to a desired functionality. A challenging
requirement concerns the visually lossless mode.
Compression is not about storage costs alone. It is
also about transmission time, imaging apparatus
utilization and the patient’s convenience/comfort.
Compression techniques by reducing file size and
transmission time can thereby improve overall care.
Image compression techniques take advantage of any
occurring redundancy. There are different redundancy
types and each compression methodology exploits
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one of these. The different redundancy types are
spatial, temporal and spectral.
Present compression schemes have high
compression rates when quality loss can be afforded.
But physicians cannot afford deficiencies in image
regions which are important, known as Regions of
Interest (ROIs). An approach which brings high
compression rates accompanied by good quality in
ROIs is needed. A common idea is to preserve quality
in diagnostically critical regions while allowing lossy
encoding of other regions. The research’s aim focuses
on ROI coding to ensure use of multiple and
arbitrarily shaped ROIs in images, with arbitrary
weights describing the importance for each ROI
including background (i.e. image regions not of ROI)
so that latter regions can be represented by varying
levels of quality.
In medical images, some structures in the
data are of interest. These structures typically occupy
a small percentage of the data, but their analysis
requires contextual information like locations within a
specific organ or adjacency to sensitive structures.
Therefore, while focusing on a particular region of the
data, designated as a Region of Interest (ROI),
contextual information surrounding that region is
important. However, the same amount of detail is not
required for the context and the ROI. Improved
Adaptive Fuzzy C-means logic is used to separate out
ROI by extracting image features. After performing
segmentation lossless compression is applied to
significant region i.e., Integer Multi Wavelet
Transform and lossy is applied to rest of the image
i.e., SPIHT. The lossless and lossy compression part
is combined and whole image is applied modified
embedded zero wavelet for improve the PSNR and
Huffman coding is applied for file transmission. The
specialist hospital will receive compressed image then
it will decompress with better quality of image.
II.
Methodology
The proposed system is a novel telemedicine
application, which meets the special requirements of
tele-ophthalmologic field using secure image
transmission via public Internet networks. It assuages
the network bandwidth bottleneck by modeling the
network bandwidth usage.
Fig.1 Block Diagram of Proposed System
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The Teleophthalmology system has two
kinds of locations. Without loss of generality, one
location is called clinic and another is called hospital.
The patient’s medical data will be captured by
instruments (e.g., camera and ophthalmoscope) in the
clinic. At the other end, the ophthalmologist will
receive the patient’s medical data and provide
consultation advice in the hospital. The clinic system
is responsible for patient registration, medical
examination, and patient consultation. It is equipped
with three monitors. The first monitor enables a clinic
physician to register the patient bio data and capture
the retinal image in real time.
The proposed system accepts input image
and produces segmented image as output. It consists
of various modules namely preprocessing unit,
segmentation, compression and decompression unit.
The proposed system starts with the input image,
preprocessing of the image is done for removing the
noise for a better segmentation. After preprocessing,
segmentation and tracking are performed. A model
fitting technique is to be proposed after tracking the
borders. The tracked borders are to be decomposed
into meaningful regional parameters. The original
image can be reconstructed from the compressed
image using inverse transforms to the above proposed
algorithm model.
2.1. Noise Removal
In order to make the image noise free,
preprocessing should be performed as the first step.
Preprocessing phase of the images is necessary to
improve the quality of the images and make the
images more reliable for further processing.
Preprocessing is always a necessity whenever the
image to be compressed in noisy, inconsistent or
incomplete and it significantly improves the
effectiveness of the image compression techniques.
Wiener filtering is a method of restoring images in the
presence of noise and blur.
2.2. Extraction of ROI
To separate out ROI from the diagnosis
image, Improved Adaptive Fuzzy C-means Clustering
Algorithm (IAFCM) segmentation has to be
performed which plays a dominant role in image
analysis. IAFCM
improves the sensitivity,
segmentation and classification accuracy in the
existing system. The concept of Improved Adaptive
Fuzzy C-means Clustering Algorithm (IAFCM) is it
uses a new objective function with a different
regulation term, which appears to be more effective in
controlling the shape of the gain field. Improved
Adaptive Fuzzy C-means Clustering Algorithm
(IAFCM) avoids solving large differential equation
and gives much faster computational speed. Improved
Adaptive Fuzzy C-means Clustering Algorithm
(IAFCM) with a new objective function yields better
background compensation and results in improved
segmentation and classification Image segmentation
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can be used in medical analysis where it is possible to
identify and analyze the defects and in compressing
some segments communication can be made possible
by saving network resource.
Clustering is a way to separate groups of
objects. C-means clustering treats each object as
having a location in space. C-means clustering
requires that you specify the number of clusters to be
partitioned and a distance metric to quantify how
close two objects are to each other. Color Base
Segmentation Using C-Means Clustering. The
solution of the above equation gives the optimum
values of (
, , ), which lead to the algorithm
described as IAFCM.
Initialize
with 1(i=1...N) and cluster centers
(k=1….NC) with random values within the
image intensity, Where NC is the number of
clusters.
Update the membership function
Update the cluster centers
Calculate the gain field
Update the gain field
Using the above equation the segmentation
of retinal images is done. The suggested approach at
first divide the image into sub regions according to
the distribution of various textural descriptors which
belongs to two main categories. First, co occurrence
matrices based features and second, coherence
analysis based measures are compared at this stage of
the proposed methodology. Each sub region is then
classified as texturally important or not utilizing fuzzy
logic unsupervised techniques. The textural features
for the sliding window size of M=8 for a 256 x 256
images are considered by calculating co occurrence
matrices and statistical metrics like Correlation,
entropy, Inverse difference moment, energy-angular
moment are calculated and coherence analysis is
performed for it. From the measures significant and
insignificant regions are determined using fuzzy logic
unsupervised techniques.
After determination of co occurrence
matrices, second method for deriving textural features
is the coherence analysis of the original image. The
coherence measures takes on low values in regions of
the textures with similar pixels and the variation is
higher in those points that are between the regions
with different textural structure. Then clustering
technique is performed to group pixels of similar
textures. Texturally significant and insignificant
patterns are grouped into labeling of two logic levels
“1” and “0”. A black and white image results for the
significant and insignificant partitions. In short, at
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first texture properties are transformed in to fuzzy set.
Appropriate fuzzy membership functions are
determined and values corresponding to it are
assigned.
2.3. Lossless Compression Technique
The Integer Multi Wavelet Transform
(IMWT) is used to have lossless processing. The
IMWT is proposed for an integer implementation of a
multi wavelet system, based on the simple multi –
scalar function.. Multi wavelet transform is
implemented by multi filter with vector sequences as
its input and output. The wavelet transform (WT), in
general, produces floating point coefficients.
Although these coefficients can be used to reconstruct
an original image perfectly in theory, the use of finite
precision arithmetic and quantization results in a lossy
scheme. The advantages of IMWT are
Higher order of approximation
High energy compaction capability
Symmetry
Dynamic range of the coefficients will not be
largely amplified
Faster calculation
2.4. Lossy Compression Technique
SPIHT is an extension of the Embedded
Zero Tree Algorithm (EZW) and was developed by
Amir Said and William Pearlman in 1996. It has been
known to give significantly impressive results in
image compression as compared to other techniques.
The SPIHT algorithm offers significantly improved
quality over other image compression techniques such
as vector quantization, JPEG and wavelets combined
with quantization. It offers characteristics such as:
Good image quality with a high PSNR
Optimized for progressive image transmission
Fast coding and decoding
Can be used for lossless compression
Can be efficiently combined with error protection
The SPIHT Algorithm works in two phases:
First the wavelet transform of the input image is
computed and then the wavelet coefficients are
transmitted to the SPIHT coding engine. After the
discrete wavelet transform of the image has been
computed, SPIHT divides the wavelets in to spatial
orientation trees. Each node in the tree corresponds to
an individual pixel. Each pixel in the transformed
image is coded on basis of its significance by
comparing with a threshold value at each level. If the
value of a pixel or any of its offspring is below the
threshold, we can conclude that all of its descendants
are insignificant at that level and need not be passed.
After each pass, the threshold is divided by two and
the algorithm proceeds further. This way information
about the most significant bits of the wavelet
coefficients will always precede information on
lower-order significant bits, which is referred to as
bit-plane ordering.
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The set partition in hierarchical trees
(SPIHT) coding algorithm is best in terms of
compression performance. Previously, the SPIHT was
designed for lossy data compression. By combining
the IMWT with the SPIHT, both the lossy and
lossless compression modes are now supported. The
major advantage of using SPIHT coding technique is
that, it supports embedded coding along with
progressive transmission, which is suitable for
telemedicine.
2.5. Modified EZW and Huffman Coding
In Shapiro’s EZW algorithm, a “zero tree”
consists of a parent and its off springs are
insignificant, then the ancestor is coded as zero trees.
If the value of the coefficient is lower than the
threshold and has one or more significant descendants
with respect to ‘j’th level, then they are coded as
“isolated zero”. The significant coefficients of the last
sub-bands, which do not accept descendants and are
not themselves descendants of a zero tree are also
considered to be zero trees. The significance symbols
of the image coefficients are then placed in the
dominant list. The amplitudes of the significant
coefficients are placed in the subordinate list. Their
values in the transformed image are set to zero in
order not to undergo the next step. Finally to the
above coefficients, Huffman coding is applied.
A bit corresponding to 2j-1 is emitted for all
the significant values in the refinement list S in order
to increase the precision of those values transmitted.
The coefficients are then converted into binary by the
coding technique. This process is repeated by
dividing the threshold by 2. The process is reiterated
until the desired quality of the reconstructed image is
reached or until the number of transferable bit
required is exceeded. The modified algorithm works
in the following way:
Symbols were added to the significance test stage
to allow a better redistribution of the entropy.
The coding of the dominant elements and the
subordinate list quantization bits was optimized.
If a coefficient is tested and found to be
significant, its off springs are also tested. If at least
one coefficient is significant, then the descendants are
coded according to the doing rules of the Shapiro’s
algorithm, which is the case for the coefficients. If a
coefficient is tested and found to be significant, its off
springs are also tested. If all the coefficients are
significant, then the descendants are coded with
symbols Pt, for positive coefficient and with symbol
Nt. Performing the above steps for the two
possibilities of coefficient values reduction in code
symbol of four results for the both the cases.
In Shapiro’s EZW algorithm, the dominant
list D is composed of four symbols, each one coded
into binary on two bits; these symbols are coded
Huffman coded before transmission. Huffman coding
assigns less codes for coefficients whose probabilities
of occurrence is high and vice versa for coefficients
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5. E. SIVASANKARI et al Int. Journal of Engineering Research and Applications
ISSN : 2248-9622, Vol. 3, Issue 6, Nov-Dec 2013, pp.881-887
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whose probabilities of occurrence is low. The
significant size is obtained by binary regrouping of
several symbols.
After the compression, the compressed
image is transmitted over TCP at bit-rate 587 kb/s.
Then the hospital system received compressed image
and was decompressed image. The performance of
proposed system
had evaluated based on
Compression Ratio (CR), peak signal to noise ratio
(PSNR), Normalized Average Error (NAE), Average
Difference (AD), Maximum Difference (MD), Mean
square
error
(MSE),Root
Mean
Square
error(RMSE),Signal
to
Noise
Ratio
(SNR),Normalized
cross
correlation
(NCC),
Structural Content (SC),Encoding and Decoding time.
Surely this will give better result compared with other
compression techniques within limited computational
complexity.
III.
Result and Discussion
Teleophthalmology targets to provide a
virtual platform for eye consultation. Hence, it should
select suitable network bandwidth for balancing the
cost and QoS, and tolerate the network bandwidth
fluctuation. ROI-based compression computational
complexity is also one of the important issues to be
considered, while addressing real time applications.
But the method of separate transforms to the two
regions proves better results compared to the ordinary
way of applying only single transforms to the whole
image. The proposed method evaluated in MATLAB
platform.
Fig. 3 Preprocessing Output
Fig.4 Segmented Output
Fig.2 Input Image
Fig. 2 shows the input of the original retinal
images where the mask for the ROI between the
important space and the non-important space is
computed. Fig.3 shows preprocessing output and
Fig.4 shows segmented output. Using EZW method,
the medical image is segmented into ROI and non
ROI, it is seen that the significantly improved
reconstruction image obtained. The figure 5 shows
analysis of compression ratio with respect to level.
The expected PSNR value is 80.The following
formulas is used to find performance of reconstructed
image.
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Fig.5 Analysis of compression ratio with respect to
level
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6. E. SIVASANKARI et al Int. Journal of Engineering Research and Applications
ISSN : 2248-9622, Vol. 3, Issue 6, Nov-Dec 2013, pp.881-887
[3]
[4]
IV.
Conclusion
This paper takes a detailed analysis of image
compression. With the growing demand for better
ROI-based Procedures for progressive transmission of
digital images: comparison bandwidth utilization,
efficient image data compression techniques have
emerged as an important factor for image data
transmission and storage. Different attributes of
compression such as compression ratio, peak signal to
noise ratio, bits per pixel can be calculated. Image
compression technique is used to reduce the number
of bits required in representing image, which helps to
reduce the storage space and transmission cost. The
proposed algorithm can compare the image quality
using different image attributes. We can conclude that
the system will provide the better visibility after the
image restoration. The implementation of ROI based
compression is providing better results as compared
with lossless algorithms, along with preservation of
diagnostically important information. Such image
compression system recommended for telemedicine.
Hence, it offers great potential of cutting down the
overall treatment costs, as it is able to maximize the
specialist productivity by having a specialist handling
many cases without having to commute to be
physically with the patients. Our real trials
demonstrate
that
the
telemedicine
via
teleophthalmology is promising.
V.
Acknowledge
The authors would like to thank Aravinth
and Arasan Eye Hospital for their contributions in
providing the eye samples. The authors are grateful to
the reviewers for their constructive comments to
improve the manuscript.
[5]
[6]
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