This document presents a new segmentation technique for brain MRI images and compares it to existing techniques. The proposed technique is a two-stage brain extraction algorithm (2D-BEA) that first removes noise and enhances brain boundaries, then uses morphological operations to extract the brain region. It is shown to accurately extract the brain from MRI images. The technique is then compared to other segmentation methods like thresholding, edge detection, fuzzy c-means clustering, and k-means clustering. The results demonstrate that the 2D-BEA technique outperforms these other methods in effectively segmenting the brain region from MRI images.
Segmentation of Brain MR Images for Tumor Extraction by Combining Kmeans Clus...CSCJournals
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Segmentation of images holds an important position in the area of image processing. It becomes more important while typically dealing with medical images where pre-surgery and post surgery decisions are required for the purpose of initiating and speeding up the recovery process [5] Computer aided detection of abnormal growth of tissues is primarily motivated by the necessity of achieving maximum possible accuracy. Manual segmentation of these abnormal tissues cannot be compared with modern dayĂ¢ââââ¢s high speed computing machines which enable us to visually observe the volume and location of unwanted tissues. A well known segmentation problem within MRI is the task of labeling voxels according to their tissue type which include White Matter (WM), Grey Matter (GM) , Cerebrospinal Fluid (CSF) and sometimes pathological tissues like tumor etc. This paper describes an efficient method for automatic brain tumor segmentation for the extraction of tumor tissues from MR images. It combines Perona and Malik anisotropic diffusion model for image enhancement and Kmeans clustering technique for grouping tissues belonging to a specific group. The proposed method uses T1, T2 and PD weighted gray level intensity images. The proposed technique produced appreciative results
Literature Survey on Detection of Brain Tumor from MRI Images IOSR Journals
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This document provides a literature survey on methods for detecting brain tumors from MRI images. It discusses several segmentation and clustering techniques that have been used for this purpose, including thresholding, edge-based segmentation, region-based segmentation, fuzzy c-means clustering, and k-means clustering. The document also reviews related work applying these methods and evaluates their effectiveness at automatically detecting and segmenting brain tumors from MRI data.
Fully Automatic Method for 3D T1-Weighted Brain Magnetic Resonance Images Seg...CSCJournals
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In the domain of medical imaging, accurate segmentation of brain MR images is of interest for many brain disorders. However, due to several factors such noise, imaging artefacts, intrinsic tissue variation and partial volume effects, tissue segmentation remains a challenging task. So, in this paper, a full automatic method for segmentation of brain MR images is presented. The method consists of four steps segmentation procedure. First, noise removing by median filtering is done; second segmentation of brain/non-brain tissue is performed by using a Threshold Morphologic Brain Extraction method (TMBE). Then initial centroids estimation by gray level histogram analysis based is executed. Finally, Fuzzy C-means Algorithm is used for MRI tissue segmentation. The efficiency of the proposed method is demonstrated by extensive segmentation experiments using simulated and real MR images.
IRJET- An Efficient Brain Tumor Detection System using Automatic Segmenta...IRJET Journal
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This document presents a proposed method for an efficient brain tumor detection system using automatic segmentation with convolutional neural networks. The proposed method uses median filtering for noise removal, Otsu's thresholding for segmentation, and morphological operations for filtering. A convolutional neural network is then used for tumor classification. The methodology is tested on a brain MRI dataset, with evaluations of performance metrics like accuracy, precision, recall, and processing time. The goal is to develop an automated system for early detection of brain tumors using deep learning techniques for analysis of medical images.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
Automatic Diagnosis of Abnormal Tumor Region from Brain Computed Tomography I...ijcseit
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The research work presented in this paper is to achieve the tissue classification and automatically
diagnosis the abnormal tumor region present in Computed Tomography (CT) images using the wavelet
based statistical texture analysis method. Comparative studies of texture analysis method are performed
for the proposed wavelet based texture analysis method and Spatial Gray Level Dependence Method
(SGLDM). Our proposed system consists of four phases i) Discrete Wavelet Decomposition (ii)
Feature extraction (iii) Feature selection (iv) Analysis of extracted texture features by classifier. A
wavelet based statistical texture feature set is derived from normal and tumor regions. Genetic Algorithm
(GA) is used to select the optimal texture features from the set of extracted texture features. We construct
the Support Vector Machine (SVM) based classifier and evaluate the performance of classifier by
comparing the classification results of the SVM based classifier with the Back Propagation Neural network
classifier(BPN). The results of Support Vector Machine (SVM), BPN classifiers for the texture analysis
methods are evaluated using Receiver Operating Characteristic (ROC) analysis. Experimental results
show that the classification accuracy of SVM is 96% for 10 fold cross validation method. The system
has been tested with a number of real Computed Tomography brain images and has achieved satisfactory
results.
IRJET - An Efficient Approach for Multi-Modal Brain Tumor Classification usin...IRJET Journal
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This paper proposes an efficient approach for multi-modal brain tumor classification using texture features and machine learning. It uses the MICCAI BraTS 2016 dataset and segments tumors using fuzzy c-means clustering. It then extracts texture features like GLCM and LBP and classifies tumors as benign or malignant using an SVM classifier. The proposed method achieved accurate segmentation and classification of brain tumors in MRI images.
Automatic Brain Tumour Detection Using Symmetry InformationIJERA Editor
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Image segmentation is used to separate an image into several âmeaningfulâ parts. Image segmentation is identification of homogeneous regions in the image. Many algorithms have been elaborated for gray scale images. However, the problem of segmentation for color images, which convey much more information about objects in scenes, has received much less attention of scientific community. While several surveys of monochrome image segmentation techniques were published, similar surveys for color images did not emerge.
Image segmentation is a process of pixel classification. An image is segmented into subsets by assigning individual pixels to classes. It is an important step towards pattern detection and recognition. Segmentation is one of the first steps in image analysis. It refers to the process of partitioning a digital image into multiple regions (sets of pixels). Each of the pixels in a region is similar with respect to some characteristic or computed property, such as color, intensity, or texture. The level of segmentation is decided by the particular characteristics of the problem being considered. Image segmentation could be further used for object matching between two images. An object of interest is specified in the first image by using the segmentation result of that image; then the specified object is matched in the second image by using the segmentation result of that image
Segmentation of Brain MR Images for Tumor Extraction by Combining Kmeans Clus...CSCJournals
Â
Segmentation of images holds an important position in the area of image processing. It becomes more important while typically dealing with medical images where pre-surgery and post surgery decisions are required for the purpose of initiating and speeding up the recovery process [5] Computer aided detection of abnormal growth of tissues is primarily motivated by the necessity of achieving maximum possible accuracy. Manual segmentation of these abnormal tissues cannot be compared with modern dayĂ¢ââââ¢s high speed computing machines which enable us to visually observe the volume and location of unwanted tissues. A well known segmentation problem within MRI is the task of labeling voxels according to their tissue type which include White Matter (WM), Grey Matter (GM) , Cerebrospinal Fluid (CSF) and sometimes pathological tissues like tumor etc. This paper describes an efficient method for automatic brain tumor segmentation for the extraction of tumor tissues from MR images. It combines Perona and Malik anisotropic diffusion model for image enhancement and Kmeans clustering technique for grouping tissues belonging to a specific group. The proposed method uses T1, T2 and PD weighted gray level intensity images. The proposed technique produced appreciative results
Literature Survey on Detection of Brain Tumor from MRI Images IOSR Journals
Â
This document provides a literature survey on methods for detecting brain tumors from MRI images. It discusses several segmentation and clustering techniques that have been used for this purpose, including thresholding, edge-based segmentation, region-based segmentation, fuzzy c-means clustering, and k-means clustering. The document also reviews related work applying these methods and evaluates their effectiveness at automatically detecting and segmenting brain tumors from MRI data.
Fully Automatic Method for 3D T1-Weighted Brain Magnetic Resonance Images Seg...CSCJournals
Â
In the domain of medical imaging, accurate segmentation of brain MR images is of interest for many brain disorders. However, due to several factors such noise, imaging artefacts, intrinsic tissue variation and partial volume effects, tissue segmentation remains a challenging task. So, in this paper, a full automatic method for segmentation of brain MR images is presented. The method consists of four steps segmentation procedure. First, noise removing by median filtering is done; second segmentation of brain/non-brain tissue is performed by using a Threshold Morphologic Brain Extraction method (TMBE). Then initial centroids estimation by gray level histogram analysis based is executed. Finally, Fuzzy C-means Algorithm is used for MRI tissue segmentation. The efficiency of the proposed method is demonstrated by extensive segmentation experiments using simulated and real MR images.
IRJET- An Efficient Brain Tumor Detection System using Automatic Segmenta...IRJET Journal
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This document presents a proposed method for an efficient brain tumor detection system using automatic segmentation with convolutional neural networks. The proposed method uses median filtering for noise removal, Otsu's thresholding for segmentation, and morphological operations for filtering. A convolutional neural network is then used for tumor classification. The methodology is tested on a brain MRI dataset, with evaluations of performance metrics like accuracy, precision, recall, and processing time. The goal is to develop an automated system for early detection of brain tumors using deep learning techniques for analysis of medical images.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
Automatic Diagnosis of Abnormal Tumor Region from Brain Computed Tomography I...ijcseit
Â
The research work presented in this paper is to achieve the tissue classification and automatically
diagnosis the abnormal tumor region present in Computed Tomography (CT) images using the wavelet
based statistical texture analysis method. Comparative studies of texture analysis method are performed
for the proposed wavelet based texture analysis method and Spatial Gray Level Dependence Method
(SGLDM). Our proposed system consists of four phases i) Discrete Wavelet Decomposition (ii)
Feature extraction (iii) Feature selection (iv) Analysis of extracted texture features by classifier. A
wavelet based statistical texture feature set is derived from normal and tumor regions. Genetic Algorithm
(GA) is used to select the optimal texture features from the set of extracted texture features. We construct
the Support Vector Machine (SVM) based classifier and evaluate the performance of classifier by
comparing the classification results of the SVM based classifier with the Back Propagation Neural network
classifier(BPN). The results of Support Vector Machine (SVM), BPN classifiers for the texture analysis
methods are evaluated using Receiver Operating Characteristic (ROC) analysis. Experimental results
show that the classification accuracy of SVM is 96% for 10 fold cross validation method. The system
has been tested with a number of real Computed Tomography brain images and has achieved satisfactory
results.
IRJET - An Efficient Approach for Multi-Modal Brain Tumor Classification usin...IRJET Journal
Â
This paper proposes an efficient approach for multi-modal brain tumor classification using texture features and machine learning. It uses the MICCAI BraTS 2016 dataset and segments tumors using fuzzy c-means clustering. It then extracts texture features like GLCM and LBP and classifies tumors as benign or malignant using an SVM classifier. The proposed method achieved accurate segmentation and classification of brain tumors in MRI images.
Automatic Brain Tumour Detection Using Symmetry InformationIJERA Editor
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Image segmentation is used to separate an image into several âmeaningfulâ parts. Image segmentation is identification of homogeneous regions in the image. Many algorithms have been elaborated for gray scale images. However, the problem of segmentation for color images, which convey much more information about objects in scenes, has received much less attention of scientific community. While several surveys of monochrome image segmentation techniques were published, similar surveys for color images did not emerge.
Image segmentation is a process of pixel classification. An image is segmented into subsets by assigning individual pixels to classes. It is an important step towards pattern detection and recognition. Segmentation is one of the first steps in image analysis. It refers to the process of partitioning a digital image into multiple regions (sets of pixels). Each of the pixels in a region is similar with respect to some characteristic or computed property, such as color, intensity, or texture. The level of segmentation is decided by the particular characteristics of the problem being considered. Image segmentation could be further used for object matching between two images. An object of interest is specified in the first image by using the segmentation result of that image; then the specified object is matched in the second image by using the segmentation result of that image
1) The document presents an integrated technique for detecting brain tumors in MRI images that combines modified texture-based region growing segmentation and edge detection.
2) The technique first performs pre-processing on MRI images, then uses modified texture-based region growing to segment regions. It then applies edge detection to extract the tumor region.
3) Experimental results show the integrated technique provides more accurate tumor detection compared to individual segmentation methods and manual segmentation.
International Refereed Journal of Engineering and Science (IRJES) is a peer reviewed online journal for professionals and researchers in the field of computer science. The main aim is to resolve emerging and outstanding problems revealed by recent social and technological change. IJRES provides the platform for the researchers to present and evaluate their work from both theoretical and technical aspects and to share their views.
A Dualistic Sub-Image Histogram Equalization Based Enhancement and Segmentati...inventy
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This document presents a dualistic sub-image histogram equalization technique for medical image enhancement and segmentation. The technique divides an image histogram into two parts based on mean and median, then equalizes each sub-histogram independently. It enhances images effectively while constraining average luminance shift. For segmentation, canny edge detection and neural networks are used. The technique is tested on medical images and shows improved completeness and correctness over previous methods, with neural networks increasing accuracy to 98.3257%.
Multistage Classification of Alzheimerâs DiseaseIJLT EMAS
Â
Alzheimerâs disease is a type of dementia that destroys
memory and other mental functions. During the progression of
the disease certain proteins called plaques and tangles get
deposited in hippocampus which is located in the temporal lobe
of brain. The disease is not a normal part of aging and gets
worsen over time. Medical imaging techniques like Magnetic
Resonance Imaging (MRI), Computed Tomography (CT) and
Positron Emission Tomography (PET) play significant role in the
disease diagnosis. In this paper, we propose a method for
classifying MRI into Normal Control (NC), Mild Cognitive
Impairment (MCI) and Alzheimerâs Disease(AD). An overall
outline of the methodology includes textural feature extraction,
feature reduction process and classification of the images into
various stages. Classification has been performed with three
classifiers namely Support Vector Machine (SVM), Artificial
Neural Network (ANN) and k-Nearest Neighbours (k-NN)
An Efficient Brain Tumor Detection Algorithm based on Segmentation for MRI Sy...ijtsrd
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A collection, or mass, of abnormal cells in the brain is called as Brain Tumor . The skull, which encloses your brain, is very rigid. Growth inside such a restricted space can cause problems. Brain tumors can be malignant or benign. Segmentation in magnetic resonance imaging (MRI) was an emergent research area in the field of medical imaging system. In this an efficient algorithm is proposed for tumor detection based on segmentation and morphological operators. Quality of scanned image is enhanced and then morphological operators are applied to detect the tumor in the scanned image. Merlin Asha. M | G. Naveen Balaji | S. Mythili | A. Karthikeyan | N. Thillaiarasu"An Efficient Brain Tumor Detection Algorithm based on Segmentation for MRI System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: http://www.ijtsrd.com/papers/ijtsrd9667.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/9667/an-efficient-brain-tumor-detection-algorithm-based-on-segmentation-for-mri-system/merlin-asha-m
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.
MRIIMAGE SEGMENTATION USING LEVEL SET METHOD AND IMPLEMENT AN MEDICAL DIAGNOS...cseij
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Image segmentation plays a vital role in image processing over the last few years. The goal of image segmentation is to cluster the pixels into salient image regions i.e., regions corresponding to individual surfaces, objects, or natural parts of objects. In this paper, we propose a medical diagnosis system by using level set method for segmenting the MRI image which investigates a new variational level set algorithm without re- initialization to segment the MRI image and to implement a competent medical diagnosis system by using MATLAB. Here we have used the speed function and the signed distance function of the image in segmentation algorithm. This system consists of thresholding technique, curve evolution technique and an eroding technique. Our proposed system was tested on some MRI Brain images, giving promising results by detecting the normal or abnormal condition specially the existence of tumers. This system will be applied to both simulated and real images with promising results
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.
This document summarizes a research paper on using bilateral symmetry analysis to detect brain tumors from MRI images. It begins by introducing the problem of brain tumor detection and importance of asymmetry analysis. It then describes the proposed algorithm which involves defining a bilateral symmetry axis between the two brain hemispheres and detecting any regions of asymmetry that could indicate a tumor. The algorithm uses edge detection techniques to find the symmetry axis. Performance is evaluated on sample patient data and results show the method can successfully identify tumor locations and sizes. In conclusion, analyzing bilateral symmetry is an effective approach for automated brain tumor detection from MRI images.
Identifying brain tumour from mri image using modified fcm and supportIAEME Publication
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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.
Comparative performance analysis of segmentation techniquesIAEME Publication
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This document compares the performance of several image segmentation techniques: global thresholding, adaptive thresholding, region growing, and level set segmentation. It applies these techniques to medical and synthetic images corrupted with noise and evaluates the segmentation results using binary classification metrics like sensitivity, specificity, accuracy, and precision. The results show that level set segmentation best preserves object boundaries, adaptive thresholding captures most image details, and global thresholding has the highest success rate at extracting regions of interest. Overall, the study aims to determine the optimal segmentation method for medical images from CT scans.
An Ameliorate Technique for Brain Lumps Detection Using Fuzzy C-Means ClusteringIRJET Journal
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This document discusses using fuzzy C-means clustering to improve detection of brain tumors in abnormal MRI images. It begins with an abstract that outlines using fuzzy clustering with local information to improve segmentation efficiency over other clustering methods. It then provides background on the importance of accurate brain tumor detection and challenges with current visual examination methods. The document proposes using a fuzzy level set algorithm for medical image segmentation and evaluation of the proposed method. It reviews various existing segmentation techniques and challenges, and suggests an improved technique using modified classifiers, feedback, and analyzing texture and shape properties with fuzzy C-means clustering for brain tumor detection and image retrieval from MRI data.
Brain Tumor Detection using MRI ImagesYogeshIJTSRD
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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
Fuzzy k c-means clustering algorithm for medical imageAlexander Decker
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This document summarizes and compares several algorithms used for medical image segmentation, including thresholding, classifiers, Markov random field models, artificial neural networks, atlas-guided approaches, deformable models, and clustering analysis methods like k-means and fuzzy c-means. It provides details on the fuzzy c-means and k-means clustering algorithms, including their process and flowcharts. A new fuzzy k-c-means algorithm is proposed that combines fuzzy c-means and k-means clustering to improve segmentation time. The algorithms are tested on MRI brain images and their results are analyzed and compared based on time, iterations, and accuracy.
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.
IRJET- A Novel Segmentation Technique for MRI Brain Tumor ImagesIRJET Journal
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This document summarizes several research papers on techniques for segmenting brain tumors in MRI images. It discusses challenges in brain tumor segmentation and describes various approaches that have been proposed, including methods using feature selection, kernel sparse representation, multiple kernel learning (MKL), and post-processing techniques. The document also reviews state-of-the-art segmentation, registration, and modeling methods for brain tumor images and their performance.
Image Binarization for the uses of Preprocessing to Detect Brain Abnormality ...Journal For Research
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Computerized MR of brain image binarization for the uses of preprocessing of features extraction and brain abnormality identification of brain has been described. Binarization is used as intermediate steps of many MR of brain normal and abnormal tissues detection. One of the main problems of MRI binarization is that many pixels of brain part cannot be correctly binarized due to the extensive black background or the large variation in contrast between background and foreground of MRI. Proposed binarization determines a threshold value using mean, variance, standard deviation and entropy followed by a non-gamut enhancement that can overcome the binarization problem. The proposed binarization technique is extensively tested with a variety of MRI and generates good binarization with improved accuracy and reduced error.
Improved Segmentation Technique for Enhancement of Biomedical ImagesIJEEE
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The aim of this paper is to develop a fast and reliable segmentation method to segment the haemorrhage region from brain CT images. To calculate area of segmented hemorrhage region that could be useful for physicians or researchers involved in the treatment or investigation of intracranial brain haemorrhage.
Efficient Brain Tumor Detection Using Wavelet TransformIJERA Editor
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Brain tumor detection is a challenging task and its very important to analyze the structure of the tumor correctly so a automatic method is used now a days for the detection of the tumor. This method saves time as well as it reduces the error which occurs in the method of manual detection. In this paper the tumor is detected using wavelet transform. MRI is an important tool used in many fields of medicine and is capable of generating a detailed image of any part of the human body. The tumor is segmented from the MRI images, features are extracted and then the area of the tumor is determined. PNN can successfully handle the process of brain tumor classification
This document discusses the importance of methodology in scientific research papers that aim to apply science and technology to address millennium challenges. It defines methodology as the framework and methods used in a research study. The document examines key components of methodology, including research design, study population, variables, sampling techniques, sample size determination, data collection methods, and data analysis. It provides examples for how to determine these methodological components and stresses that applying the appropriate methodology is essential for producing valid, high-quality research that can help solve important problems.
This document presents a new approach for human identification using sclera recognition. It begins with background on sclera and challenges with sclera recognition. It then describes the proposed methodology which includes sclera segmentation, feature extraction using Gabor filtering, and recognition using Bayesian classification. Experimental results show the false accept and reject rates for the approach. It concludes that sclera recognition is promising for human identification and can achieve accuracy comparable to iris recognition in visible light. The proposed approach uses Bayesian classification for recognition, which is more effective than previous matching score methods.
1) The document presents an integrated technique for detecting brain tumors in MRI images that combines modified texture-based region growing segmentation and edge detection.
2) The technique first performs pre-processing on MRI images, then uses modified texture-based region growing to segment regions. It then applies edge detection to extract the tumor region.
3) Experimental results show the integrated technique provides more accurate tumor detection compared to individual segmentation methods and manual segmentation.
International Refereed Journal of Engineering and Science (IRJES) is a peer reviewed online journal for professionals and researchers in the field of computer science. The main aim is to resolve emerging and outstanding problems revealed by recent social and technological change. IJRES provides the platform for the researchers to present and evaluate their work from both theoretical and technical aspects and to share their views.
A Dualistic Sub-Image Histogram Equalization Based Enhancement and Segmentati...inventy
Â
This document presents a dualistic sub-image histogram equalization technique for medical image enhancement and segmentation. The technique divides an image histogram into two parts based on mean and median, then equalizes each sub-histogram independently. It enhances images effectively while constraining average luminance shift. For segmentation, canny edge detection and neural networks are used. The technique is tested on medical images and shows improved completeness and correctness over previous methods, with neural networks increasing accuracy to 98.3257%.
Multistage Classification of Alzheimerâs DiseaseIJLT EMAS
Â
Alzheimerâs disease is a type of dementia that destroys
memory and other mental functions. During the progression of
the disease certain proteins called plaques and tangles get
deposited in hippocampus which is located in the temporal lobe
of brain. The disease is not a normal part of aging and gets
worsen over time. Medical imaging techniques like Magnetic
Resonance Imaging (MRI), Computed Tomography (CT) and
Positron Emission Tomography (PET) play significant role in the
disease diagnosis. In this paper, we propose a method for
classifying MRI into Normal Control (NC), Mild Cognitive
Impairment (MCI) and Alzheimerâs Disease(AD). An overall
outline of the methodology includes textural feature extraction,
feature reduction process and classification of the images into
various stages. Classification has been performed with three
classifiers namely Support Vector Machine (SVM), Artificial
Neural Network (ANN) and k-Nearest Neighbours (k-NN)
An Efficient Brain Tumor Detection Algorithm based on Segmentation for MRI Sy...ijtsrd
Â
A collection, or mass, of abnormal cells in the brain is called as Brain Tumor . The skull, which encloses your brain, is very rigid. Growth inside such a restricted space can cause problems. Brain tumors can be malignant or benign. Segmentation in magnetic resonance imaging (MRI) was an emergent research area in the field of medical imaging system. In this an efficient algorithm is proposed for tumor detection based on segmentation and morphological operators. Quality of scanned image is enhanced and then morphological operators are applied to detect the tumor in the scanned image. Merlin Asha. M | G. Naveen Balaji | S. Mythili | A. Karthikeyan | N. Thillaiarasu"An Efficient Brain Tumor Detection Algorithm based on Segmentation for MRI System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: http://www.ijtsrd.com/papers/ijtsrd9667.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/9667/an-efficient-brain-tumor-detection-algorithm-based-on-segmentation-for-mri-system/merlin-asha-m
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.
MRIIMAGE SEGMENTATION USING LEVEL SET METHOD AND IMPLEMENT AN MEDICAL DIAGNOS...cseij
Â
Image segmentation plays a vital role in image processing over the last few years. The goal of image segmentation is to cluster the pixels into salient image regions i.e., regions corresponding to individual surfaces, objects, or natural parts of objects. In this paper, we propose a medical diagnosis system by using level set method for segmenting the MRI image which investigates a new variational level set algorithm without re- initialization to segment the MRI image and to implement a competent medical diagnosis system by using MATLAB. Here we have used the speed function and the signed distance function of the image in segmentation algorithm. This system consists of thresholding technique, curve evolution technique and an eroding technique. Our proposed system was tested on some MRI Brain images, giving promising results by detecting the normal or abnormal condition specially the existence of tumers. This system will be applied to both simulated and real images with promising results
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.
This document summarizes a research paper on using bilateral symmetry analysis to detect brain tumors from MRI images. It begins by introducing the problem of brain tumor detection and importance of asymmetry analysis. It then describes the proposed algorithm which involves defining a bilateral symmetry axis between the two brain hemispheres and detecting any regions of asymmetry that could indicate a tumor. The algorithm uses edge detection techniques to find the symmetry axis. Performance is evaluated on sample patient data and results show the method can successfully identify tumor locations and sizes. In conclusion, analyzing bilateral symmetry is an effective approach for automated brain tumor detection from MRI images.
Identifying brain tumour from mri image using modified fcm and supportIAEME Publication
Â
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.
Comparative performance analysis of segmentation techniquesIAEME Publication
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This document compares the performance of several image segmentation techniques: global thresholding, adaptive thresholding, region growing, and level set segmentation. It applies these techniques to medical and synthetic images corrupted with noise and evaluates the segmentation results using binary classification metrics like sensitivity, specificity, accuracy, and precision. The results show that level set segmentation best preserves object boundaries, adaptive thresholding captures most image details, and global thresholding has the highest success rate at extracting regions of interest. Overall, the study aims to determine the optimal segmentation method for medical images from CT scans.
An Ameliorate Technique for Brain Lumps Detection Using Fuzzy C-Means ClusteringIRJET Journal
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This document discusses using fuzzy C-means clustering to improve detection of brain tumors in abnormal MRI images. It begins with an abstract that outlines using fuzzy clustering with local information to improve segmentation efficiency over other clustering methods. It then provides background on the importance of accurate brain tumor detection and challenges with current visual examination methods. The document proposes using a fuzzy level set algorithm for medical image segmentation and evaluation of the proposed method. It reviews various existing segmentation techniques and challenges, and suggests an improved technique using modified classifiers, feedback, and analyzing texture and shape properties with fuzzy C-means clustering for brain tumor detection and image retrieval from MRI data.
Brain Tumor Detection using MRI ImagesYogeshIJTSRD
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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
Fuzzy k c-means clustering algorithm for medical imageAlexander Decker
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This document summarizes and compares several algorithms used for medical image segmentation, including thresholding, classifiers, Markov random field models, artificial neural networks, atlas-guided approaches, deformable models, and clustering analysis methods like k-means and fuzzy c-means. It provides details on the fuzzy c-means and k-means clustering algorithms, including their process and flowcharts. A new fuzzy k-c-means algorithm is proposed that combines fuzzy c-means and k-means clustering to improve segmentation time. The algorithms are tested on MRI brain images and their results are analyzed and compared based on time, iterations, and accuracy.
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.
IRJET- A Novel Segmentation Technique for MRI Brain Tumor ImagesIRJET Journal
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This document summarizes several research papers on techniques for segmenting brain tumors in MRI images. It discusses challenges in brain tumor segmentation and describes various approaches that have been proposed, including methods using feature selection, kernel sparse representation, multiple kernel learning (MKL), and post-processing techniques. The document also reviews state-of-the-art segmentation, registration, and modeling methods for brain tumor images and their performance.
Image Binarization for the uses of Preprocessing to Detect Brain Abnormality ...Journal For Research
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Computerized MR of brain image binarization for the uses of preprocessing of features extraction and brain abnormality identification of brain has been described. Binarization is used as intermediate steps of many MR of brain normal and abnormal tissues detection. One of the main problems of MRI binarization is that many pixels of brain part cannot be correctly binarized due to the extensive black background or the large variation in contrast between background and foreground of MRI. Proposed binarization determines a threshold value using mean, variance, standard deviation and entropy followed by a non-gamut enhancement that can overcome the binarization problem. The proposed binarization technique is extensively tested with a variety of MRI and generates good binarization with improved accuracy and reduced error.
Improved Segmentation Technique for Enhancement of Biomedical ImagesIJEEE
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The aim of this paper is to develop a fast and reliable segmentation method to segment the haemorrhage region from brain CT images. To calculate area of segmented hemorrhage region that could be useful for physicians or researchers involved in the treatment or investigation of intracranial brain haemorrhage.
Efficient Brain Tumor Detection Using Wavelet TransformIJERA Editor
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Brain tumor detection is a challenging task and its very important to analyze the structure of the tumor correctly so a automatic method is used now a days for the detection of the tumor. This method saves time as well as it reduces the error which occurs in the method of manual detection. In this paper the tumor is detected using wavelet transform. MRI is an important tool used in many fields of medicine and is capable of generating a detailed image of any part of the human body. The tumor is segmented from the MRI images, features are extracted and then the area of the tumor is determined. PNN can successfully handle the process of brain tumor classification
This document discusses the importance of methodology in scientific research papers that aim to apply science and technology to address millennium challenges. It defines methodology as the framework and methods used in a research study. The document examines key components of methodology, including research design, study population, variables, sampling techniques, sample size determination, data collection methods, and data analysis. It provides examples for how to determine these methodological components and stresses that applying the appropriate methodology is essential for producing valid, high-quality research that can help solve important problems.
This document presents a new approach for human identification using sclera recognition. It begins with background on sclera and challenges with sclera recognition. It then describes the proposed methodology which includes sclera segmentation, feature extraction using Gabor filtering, and recognition using Bayesian classification. Experimental results show the false accept and reject rates for the approach. It concludes that sclera recognition is promising for human identification and can achieve accuracy comparable to iris recognition in visible light. The proposed approach uses Bayesian classification for recognition, which is more effective than previous matching score methods.
This document describes a sketch-based image retrieval system that uses freehand sketches as queries to retrieve similar colored images from a database. The system first extracts features like color, texture, and shape from the sketch using descriptors such as Color and Edge Directivity Descriptor (CEDD) and Edge Histogram Descriptor (EHD). It then clusters the images in the database using k-means clustering based on the similarity of their features to the sketch. Finally, the system retrieves the most similar colored image from the clustered images as the output match for the user's sketch query.
This document discusses the design, analysis, and feasibility testing of a center-mounted suspension system. It begins with an introduction to conventional suspension systems and their limitations. The proposed center-mounted system aims to improve vehicle balance in all terrains by directly attaching the suspension to the vehicle's central chassis. The document then reviews different suspension system types and analyzes the proposed system's working principles and mathematical calculations. Finally, stress analysis using ANSYS software demonstrates the advantages of the center-mounted design in absorbing shocks during turns and on bumpy roads. In conclusion, the proposed system maintains vehicle balance better than conventional designs through its unique center-attached configuration.
This document discusses different algorithms for task scheduling in cloud computing environments based on various quality of service (QoS) parameters. It summarizes several QoS-based scheduling algorithms including QDA, Improved Cost Based, PAPRIKA, ANT Colony, CMultiQoSSchedule, and SHEFT Workflow. It also provides a comparative table of these algorithms and discusses the various metrics considered by QoS-based scheduling algorithms like time, cost, makespan, trust, and resource utilization. The paper concludes that scheduling is an important factor for cloud environments and that existing algorithms can be improved by considering additional parameters like trust values, execution rates, and success rates.
This document analyzes the effect of different mobility patterns on the AODV and OLSR routing protocols in a mobile ad hoc network (MANET) using various TCP variants. It simulates scenarios using the OPNET simulator with 60 nodes under static and random waypoint mobility models. The performance is evaluated in terms of packet end-to-end delay, traffic received, and throughput. The results show that the SACK TCP variant performs best under random waypoint mobility for both protocols, while Tahoe performs best under static mobility for OLSR. It also finds that AODV generally outperforms OLSR and that SACK is the best variant for AODV across both mobility patterns.
This document summarizes research on the numerical and experimental study of the effect of impeller design on the performance of submerged turbines. A Gorlov helical water turbine was designed, fabricated, and tested both theoretically using computational fluid dynamics software and experimentally in an open channel. The experimental results showed that power increased with water velocity, reaching 4.621 W at a velocity of 1.81 m/s. CFD modeling using Fluent agreed well with experimental results. The study evaluated turbine performance at various water velocities to optimize power extraction based on impeller design.
The document discusses an energy efficient geographic routing protocol called Energy Efficient Geographic Adaptive Fidelity (EEGAF) for wireless sensor networks. It summarizes the basic Geographic Adaptive Fidelity (GAF) protocol and then proposes EEGAF, which improves on GAF in two ways: 1) It enhances the discovery phase to reduce energy used by nodes during discovery. 2) It uses a location-aware multicast routing protocol called Location Aided Routing (LAR) for data transmission, which decreases energy consumption and optimizes network lifetime. The document evaluates EEGAF using MATLAB simulations and finds it performs better than GAF in terms of network lifetime, energy efficiency, and quality of service metrics like throughput and routing overhead.
This document summarizes a paper that presents a novel method for determining the optimal location of Flexible AC Transmission System (FACTS) controllers in a multi-machine power system using a Fuzzy Controlled Genetic Algorithm (FCGA). The proposed algorithm aims to simultaneously optimize the location, type, and rated values of FACTS controllers while minimizing the overall system cost, which includes generation and investment costs. The algorithm is tested on IEEE 14-bus and 30-bus test systems, incorporating thyristor-controlled series compensator (TCSC) and unified power flow controller (UPFC) devices. Simulation results show the obtained solution is feasible and accurate for solving the optimal power flow problem.
1) The document presents the results of a stability analysis of a reinforced earth wall for the approach road of a rail over bridge along a national highway.
2) The maximum height of the embankment is 7m, and stability is a concern due to weak subgrade soil. Stability analysis using Slope/W software indicates a factor of safety below recommended values without reinforcement.
3) The addition of geosynthetic reinforcement in the form of geogrids placed at various depths and tensions within the embankment fill improves the factor of safety. The optimum reinforcement configuration is found to be 3 geogrid layers.
This document proposes a bandwidth degradation technique to reduce call dropping probability in mobile
networks. It aims to dynamically adjust bandwidth allocation to multiple users according to network conditions
to increase utilization. The technique allows for degrading the quality of existing calls to admit new calls
while maintaining quality of service. Key performance metrics analyzed include degradation ratio, degraded
bandwidth, throughput, and propagation delay. The approach is intended to be implemented using MATLAB
to simulate various mobility patterns for verification.
This document describes the design and fabrication of a prototype for testing the durability of seat belt retractors. The current testing machine has limitations like not being able to test retractors at different mounting angles and extract/retract lengths. The new prototype aims to address these using a stepper motor, spring system instead of bungee, and sensors to detect the snatch produced every 4 cycles. It involves calculations to select components like the pneumatic cylinder, FRL unit, spring, and shaft based on the required forces, strokes and flows. A 3D model was developed and simulations conducted to validate the design. The physical prototype was then fabricated to cater to increasing demand for seat belt testing.
This document summarizes an article that optimizes the Okumura-Hata propagation model for 800MHz radio frequency in Yaounde, Cameroon using a Newton second order iterative algorithm. Radio measurements were collected through drive tests on an existing CDMA2000 network in Yaounde. The root mean squared error between predicted and measured values was calculated to validate the optimized model. A new model developed through this process was found to better represent the local environment compared to the standard Okumura-Hata model. The optimized model can be used for future radio network planning in Yaounde, such as for upcoming LTE deployment in the 800MHz band.
This document summarizes research into improving transient stability in power transmission systems using a Static VAR Compensator (SVC) with a hybrid PI-Fuzzy Logic controller. It begins with an introduction to Flexible AC Transmission Systems (FACTS) and the role of SVC devices in voltage control and reactive power compensation. It then describes modeling an SVC and the operating principles of conventional PI control. The limitations of PI control for nonlinear systems are discussed. The document proposes a hybrid PI-Fuzzy Logic controller to combine the advantages of both. Simulation results using MATLAB on a 2-machine 3-bus test system show the hybrid controller improves performance during disturbances over PI or Fuzzy Logic control alone.
This document summarizes a survey on multiple patient data semantic conflicts and methods of electronically exchanging data. It discusses how heterogeneous healthcare systems can have different data formats, terminology, and semantics, leading to conflicts. It reviews literature on standardizing data using controlled terminologies and archetypes. Methods for resolving semantic conflicts include ontology mapping and mediation between standards like HL7 Version 2 and 3. Semantic conflicts can occur at the data or schema level and involve issues like naming inconsistencies or representing the same concept differently.
This document summarizes a study on the body composition of children participating in regular football, cricket, and gymnastics training. The study aimed to compare the anthropometric and body composition status of children in these three sports. Body composition measurements including body fat percentage, fat mass, and lean mass were taken for children in each sport. Statistical analysis found that footballers had significantly lower body fat percentage and fat mass than cricketers but did not differ significantly in lean mass. Footballers also had significantly lower body fat percentage and fat mass than cricketers as well as significantly higher lean mass. Gymnasts had significantly lower body fat percentage and fat mass than cricketers but did not differ significantly in lean mass. The study concluded that footballers generally had a better body
This document provides a review of information technology implementation for the educational development of rural India. It discusses several key points:
1) It provides an overview of the Indian education system, including the roles of public and private sectors as well as various supporting institutions.
2) It identifies several problems faced by students in rural areas, such as lack of adequate teachers and infrastructure like classrooms and toilets.
3) It discusses how information and communication technologies (ICT) like computers, internet, mobile phones can help improve quality of education through distance learning programs and training teachers.
4) It outlines several approaches that have been used to promote education in rural India using ICT, including village knowledge centers, e
This document summarizes a survey on balancing network load using geographic hash tables. It discusses how geographic hash tables are used to store and retrieve data from nodes in a wireless network. Two approaches to balancing the network load are proposed: 1) An analytical approach that adds new nodes to servers when load exceeds thresholds. 2) A heuristic approach that moves data between nodes to prevent any single node from receiving too many requests. The approaches aim to extend network life by distributing load more evenly without changing underlying georouting protocols.
This document proposes laying fiber-optic cables underwater along the River Nile to connect cities in Sudan. It notes that the River Nile and its tributaries pass through many Sudanese cities, providing a natural pathway. Laying cables underwater would be more cost-effective than overland routes due to avoiding expenses like drilling and land permits. It suggests constructing monitoring centers every 100km and connecting cities within that distance to create a network with significant cost savings over traditional methods. In conclusion, an underwater fiber-optic network along the River Nile could efficiently connect inland Sudanese cities and provide benefits over satellite or overland cable routes.
The document describes a method for image fusion and optimization using stationary wavelet transform and particle swarm optimization. It summarizes that image fusion combines information from multiple images to extract relevant information. The proposed method uses stationary wavelet transform for image decomposition and particle swarm optimization to optimize the fused results. It applies stationary wavelet transform to source images to decompose them into wavelet coefficients. Particle swarm optimization is then used to optimize the transformed images. The inverse stationary wavelet transform is applied to the optimized coefficients to generate the fused image. The method is tested on various images and performance is evaluated using metrics like peak signal-to-noise ratio, entropy, mean square error and standard deviation.
This document provides a literature survey on methods for detecting brain tumors from MRI images. It discusses several segmentation techniques that have been used for this purpose, including thresholding, edge-based, region-based, k-means clustering, fuzzy c-means clustering, and optimization methods like ant colony optimization, genetic algorithms, and particle swarm optimization. The document reviews related work comparing these methods and evaluates their performance based on metrics like PSNR and RMSE. It concludes that while no single universal method exists, fuzzy c-means is well-suited for medical image segmentation tasks due to its simplicity and ability to provide faster clustering.
IRJET - Clustering Algorithm for Brain Image SegmentationIRJET Journal
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The document presents a clustering algorithm for brain image segmentation using fuzzy c-means clustering. It aims to optimize the segmentation process and achieve higher accuracy rates when segmenting human MRI brain images. The fuzzy c-means algorithm is combined with rough set theory for segmentation. The algorithm segments images into homogeneous regions where adjacent regions are heterogeneous. This approach is evaluated on a set of brain images and demonstrates effectiveness as well as a comparison to other related algorithms. The goal of the algorithm is to simplify images and extract useful information for detecting brain tumors.
Survey on Brain MRI Segmentation TechniquesEditor IJMTER
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Image segmentation is aimed at cutting out, a ROI (Region of Interest) from an image. For
medical images, segmentation is done for: studying the anatomical structure, identifying ROI ie tumor
or any other abnormalities, identifying the increase in tissue volume in a region, treatment planning.
Currently there are many different algorithms available for image segmentation. This paper lists and
compares some of them. Each has their own advantages and limitations.
IRJET-A Review on Brain Tumor Detection using BFCFCM AlgorithmIRJET Journal
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The document presents a review of brain tumor detection using the BFCFCM clustering algorithm. It begins with an introduction to brain tumors and MRI imaging. It then reviews several existing techniques for brain tumor detection using artificial neural networks, linear discriminant analysis, neuro-fuzzy systems, and region growing segmentation with watershed algorithms. The document proposes a method using pre-processing, skull masking, segmentation with an advanced fuzzy c-means algorithm, feature extraction through thresholding, and an SVM classifier. Segmentation partitions the MRI image into regions/objects of interest like the tumor. Feature extraction analyzes the segmented regions to characterize the tumor for classification.
Classification of Abnormalities in Brain MRI Images Using PCA and SVMIJERA Editor
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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.
International Refereed Journal of Engineering and Science (IRJES)irjes
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International Refereed Journal of Engineering and Science (IRJES) is a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications
Segmentation of Tumor Region in MRI Images of Brain using Mathematical Morpho...CSCJournals
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This paper introduces an efficient detection of brain tumor from cerebral MRI images. The methodology consists of two steps: enhancement and segmentation. To improve the quality of images and limit the risk of distinct regions fusion in the segmentation phase an enhancement process is applied. We applied mathematical morphology to increase the contrast in MRI images and to segment MRI images. Some of experimental results on brain images show the feasibility and the performance of the proposed approach.
DIRECTIONAL CLASSIFICATION OF BRAIN TUMOR IMAGES FROM MRI USING CNN-BASED DEE...IRJET Journal
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This document presents research on using a convolutional neural network (CNN) model for the detection and classification of brain tumors from MRI images. The CNN model improves the accuracy of tumor detection and can serve as a useful tool for physicians. The researchers trained and tested several CNN architectures, including CNN, ResNet50, MobileNetV2, and VGG19 on an MRI brain image database. Their proposed model uses a modified Residual U-Net architecture with residual blocks and attention gates to better segment tumors and extract local features from MRI images. Evaluation results found their model achieved better accuracy than existing methods like U-Net and CNN for brain tumor segmentation tasks.
Overview of convolutional neural networks architectures for brain tumor segm...IJECEIAES
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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.
Brain Tumor Detection and Segmentation using UNETIRJET Journal
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This document discusses brain tumor detection and segmentation using the UNET model. It analyzes previous research on brain tumor segmentation techniques and their limitations. The proposed method uses the BraTS 2020 dataset containing 369 MRI images for training and 125 for testing. It develops a 3D UNET model for multimodal brain tumor segmentation. The model generates 3D outputs and achieves 98.5% accuracy in segmenting whole, core and enhancing tumors.
This paper primarily focuses on to employ a novel approach to classify the brain tumor and its area. The Tumor is an uncontrolled enlargement of tissues in any portion of the human body. Tumors are of several types and have some different characteristics. According to their characteristics some of them are avoidable and some are unavoidable. Brain tumor is serious and life threatening issues now days, because of todayâs hectic lifestyle. Medical imaging play important role to diagnose brain tumor .In this study an automated system has been proposed to detect and calculate the area of tumor. For proposed system the experiment carried out with 150 T1 weighted MRI images. The edge based segmentation, watershed segmentation has applied for tumor, and watershed segmentation has used to extract abnormal cells from the normal cells to get the tumor identification of involved and noninvolved areas so that the radiologist differentiate the affected area. The experiment result shows tumor extraction and area of tumor find the weather it is benign and malignant.
IRJET- Image Processing for Brain Tumor Segmentation and ClassificationIRJET Journal
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This document presents a method for segmenting and classifying brain tumors in MR images using image processing techniques. It involves pre-processing images using adaptive histogram equalization, extracting features using discrete wavelet transform (DWT) and principal component analysis (PCA) for dimension reduction. Texture and statistical features are then extracted and classifiers like support vector machine (SVM), K-nearest neighbors (KNN) and neural networks are used to classify tumors as benign, malignant or pituitary. The method is evaluated on a brain tumor dataset containing MR images of different tumor types and shows promise for automatic brain tumor segmentation and classification.
IRJET - Detection of Heamorrhage in Brain using Deep LearningIRJET Journal
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This document presents a method for detecting hemorrhage in brain CT scans using deep learning. It begins with an introduction to brain hemorrhage and the need for automated detection. Previous related work using various segmentation and classification methods is summarized. Deep learning is identified as a promising technique due to its ability to extract complex features from images. The proposed method uses a convolutional neural network model with several convolutional, max pooling, dropout and dense layers to classify brain CT scans as either normal or hemorrhagic. The model is trained on 180 images and tested on 20 images, achieving an accuracy of 94.4% at predicting hemorrhage. The method provides a fast and automated way to detect hemorrhage in brain CT scans to help
Brain Tumor Diagnosis using Image De Noising with Scale Invariant Feature Tra...ijtsrd
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It is truly challenging for specialists to distinguish mind growth at a beginning phase. X ray pictures are more helpless to the commotion and other natural aggravations. Subsequently, it becomes challenging for specialists to decide on brain tumor and their causes. Thus, we thought of a framework in which the framework will recognize mind growth from pictures. Here we are switching a picture over completely to a grayscale picture. We apply channels to the picture to eliminate commotion and other natural messes from the picture. The framework will deal with the chosen picture utilizing preprocessing steps. Simultaneously, various calculations are utilized to distinguish the growth from the picture. In any case, the edges of the picture wont be sharp in the beginning phases of cerebrum growth. So here we are applying picture division to the picture to recognize the edges of the pictures. We have proposed a picture division process and an assortment of picture separating procedures to get picture qualities. Through this whole interaction, exactness can be moved along. This framework is carried out in Matlab R2021a. The accuracy, Review, F1 Score, and Precision worth of the proposed model works by 0.16 , 1.99 , 0.47 , and 0.28 for CNN Model. Namit Thakur | Dr. Sunil Phulre "Brain Tumor Diagnosis using Image De-Noising with Scale Invariant Feature Transform" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-7 , December 2022, URL: https://www.ijtsrd.com/papers/ijtsrd52272.pdf Paper URL: https://www.ijtsrd.com/medicine/other/52272/brain-tumor-diagnosis-using-image-denoising-with-scale-invariant-feature-transform/namit-thakur
FUZZY SEGMENTATION OF MRI CEREBRAL TISSUE USING LEVEL SET ALGORITHMAM Publications
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The current study investigated a median filter with the fuzzy level set method to propose fuzzy segmentation of magnetic resonance imaging (MRI) cerebral tissue images. An MRI image was used as an input image. A median filter and fuzzy c-means (FCM) clustering were utilized to remove image noise and create image clusters, respectively. The image clusters showed initial and final cluster centers. The level set method was then used for segmentation after separating and extracting white matter from gray matter. Fuzzy c-means was sensitive to the choice of the initial cluster center. Improper center selection caused the method to produce suboptimal solutions. The proposed algorithm was successfully utilized to segment MRI cerebral tissue images. The algorithm efficiently performed segmentation of test MRI cerebral tissue images compared with algorithms proposed in previous studies.
MRI Image Segmentation Using Level Set Method and Implement an Medical Diagno...CSEIJJournal
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Image segmentation plays a vital role in image processing over the last few years. The goal of image
segmentation is to cluster the pixels into salient image regions i.e., regions corresponding to individual
surfaces, objects, or natural parts of objects. In this paper, we propose a medical diagnosis system by using
level set method for segmenting the MRI image which investigates a new variational level set algorithm
without re- initialization to segment the MRI image and to implement a competent medical diagnosis
system by using MATLAB. Here we have used the speed function and the signed distance function of the
image in segmentation algorithm. This system consists of thresholding technique, curve evolution technique
and an eroding technique. Our proposed system was tested on some MRI Brain images, giving promising
results by detecting the normal or abnormal condition specially the existence of tumers. This system will be
applied to both simulated and real images with promising results.
11.texture feature based analysis of segmenting soft tissues from brain ct im...Alexander Decker
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This document describes a study that used texture feature analysis and a bidirectional associative memory (BAM) type artificial neural network to segment normal and tumor tissues from brain CT images. Gray level co-occurrence matrix features were extracted from 80 CT images of normal, benign and malignant tumors. The most discriminative features were selected using t-tests and used to train the BAM network classifier to segment tissues in the images. The proposed method provided accurate segmentation of normal and tumor regions, especially small tumors, in an efficient and fast manner with less computational time compared to other methods.
MRI Image Segmentation Using Gradient Based Watershed Transform In Level Set ...IJERA Editor
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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.
This document summarizes a research paper that proposes an algorithm for detecting brain tumors in MRI images based on analyzing bilateral symmetry. The algorithm first performs preprocessing like smoothing and contrast enhancement. It then identifies the bilateral symmetry axis of the brain. Next, it segments the image into symmetric regions, enhancing asymmetric edges that may indicate a tumor. Experiments showed the algorithm can automatically detect tumor positions and boundaries. The algorithm leverages the fact that brain MRI of a healthy person is nearly bilaterally symmetric, while a tumor disrupts this symmetry.
IRJET - Machine Learning Applications on Cancer Prognosis and PredictionIRJET Journal
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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.
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.
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.
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Session 1 - Intro to Robotic Process Automation.pdfUiPathCommunity
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đ Check out our full 'Africa Series - Automation Student Developers (EN)' page to register for the full program:
https://bit.ly/Automation_Student_Kickstart
In this session, we shall introduce you to the world of automation, the UiPath Platform, and guide you on how to install and setup UiPath Studio on your Windows PC.
đ Detailed agenda:
What is RPA? Benefits of RPA?
RPA Applications
The UiPath End-to-End Automation Platform
UiPath Studio CE Installation and Setup
đť Extra training through UiPath Academy:
Introduction to Automation
UiPath Business Automation Platform
Explore automation development with UiPath Studio
đ Register here for our upcoming Session 2 on June 20: Introduction to UiPath Studio Fundamentals: https://community.uipath.com/events/details/uipath-lagos-presents-session-2-introduction-to-uipath-studio-fundamentals/
Essentials of Automations: Exploring Attributes & Automation ParametersSafe Software
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Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as âkeysâ). In fact, itâs unlikely youâll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, theyâll also be making use of the Split-Merge Block functionality.
Youâll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
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This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
âTemporal Event Neural Networks: A More Efficient Alternative to the Transfor...Edge AI and Vision Alliance
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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the âTemporal Event Neural Networks: A More Efficient Alternative to the Transformerâ tutorial at the May 2024 Embedded Vision Summit.
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Integration with BrainChipâs Akida neuromorphic hardware IP further enhances TENNsâ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
"What does it really mean for your system to be available, or how to define w...Fwdays
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We will talk about system monitoring from a few different angles. We will start by covering the basics, then discuss SLOs, how to define them, and why understanding the business well is crucial for success in this exercise.
"Scaling RAG Applications to serve millions of users", Kevin GoedeckeFwdays
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How we managed to grow and scale a RAG application from zero to thousands of users in 7 months. Lessons from technical challenges around managing high load for LLMs, RAGs and Vector databases.
"Choosing proper type of scaling", Olena SyrotaFwdays
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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.
Must Know Postgres Extension for DBA and Developer during MigrationMydbops
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Mydbops Opensource Database Meetup 16
Topic: Must-Know PostgreSQL Extensions for Developers and DBAs During Migration
Speaker: Deepak Mahto, Founder of DataCloudGaze Consulting
Date & Time: 8th June | 10 AM - 1 PM IST
Venue: Bangalore International Centre, Bangalore
Abstract: Discover how PostgreSQL extensions can be your secret weapon! This talk explores how key extensions enhance database capabilities and streamline the migration process for users moving from other relational databases like Oracle.
Key Takeaways:
* Learn about crucial extensions like oracle_fdw, pgtt, and pg_audit that ease migration complexities.
* Gain valuable strategies for implementing these extensions in PostgreSQL to achieve license freedom.
* Discover how these key extensions can empower both developers and DBAs during the migration process.
* Don't miss this chance to gain practical knowledge from an industry expert and stay updated on the latest open-source database trends.
Mydbops Managed Services specializes in taking the pain out of database management while optimizing performance. Since 2015, we have been providing top-notch support and assistance for the top three open-source databases: MySQL, MongoDB, and PostgreSQL.
Our team offers a wide range of services, including assistance, support, consulting, 24/7 operations, and expertise in all relevant technologies. We help organizations improve their database's performance, scalability, efficiency, and availability.
Contact us: info@mydbops.com
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Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
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An English đŹđ§ translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech đ¨đż version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Northern Engraving | Modern Metal Trim, Nameplates and Appliance PanelsNorthern Engraving
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What began over 115 years ago as a supplier of precision gauges to the automotive industry has evolved into being an industry leader in the manufacture of product branding, automotive cockpit trim and decorative appliance trim. Value-added services include in-house Design, Engineering, Program Management, Test Lab and Tool Shops.
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
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The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
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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/
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
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I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
"$10 thousand per minute of downtime: architecture, queues, streaming and fin...Fwdays
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Direct losses from downtime in 1 minute = $5-$10 thousand dollars. Reputation is priceless.
As part of the talk, we will consider the architectural strategies necessary for the development of highly loaded fintech solutions. We will focus on using queues and streaming to efficiently work and manage large amounts of data in real-time and to minimize latency.
We will focus special attention on the architectural patterns used in the design of the fintech system, microservices and event-driven architecture, which ensure scalability, fault tolerance, and consistency of the entire system.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
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Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
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?
PoznanĚ ACE event - 19.06.2024 Team 24 Wrapup slidedeck
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C1103041623
1. IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE)
e-ISSN: 2278-1676,p-ISSN: 2320-3331, Volume 11, Issue 3 Ver. IV (May. â Jun. 2016), PP 16-23
www.iosrjournals.org
DOI: 10.9790/1676-1103041623 www.iosrjournals.org 16 | Page
An Effective Segmentation Technique for Brain Medical
Resonance Images
Vandana J. Shah1
,Dr. Vijay Chourasia2
,Dr. Ravindra Kshirsagar3
1
Research Scholar, Manoharbhai Patel Institute of Engineering & Technology , Gondia
2
Assistant Professor, Manoharbhai Patel Institute of Engineering & Technology , Gondia
3
Principal, Priyadarshini Indira Gandhi College of Engineering, Nagpur
Abstract: In the field of medical resonance image processing the image segmentation is an important and
challenging problem in an image analysis. The main purpose is to diagnose the problems in the normal brain
anatomy and to find the location of tumour. Many of the algorithms have been found in recent years which aid
to segment the medical images and identify the diseases. This paper aims to compare previous algorithms for
image segmentation and also identifies the better algorithm to extract the MRI brain image for further
processing. Three types of MRI images are produced for brains which are based on proton density (PD),
longitudinal relaxation time (T1) and transverse relaxation time (T2). Specialists continuously mix multispectral
MRI information of a patient to require a call on the location, extension and prognosis and diagnose the brain
abnormalities. Neuroradiological research consists of several brain extraction algorithms which are useful for
several post- automatic image processing operations like segmentation, registration and compression. The
result of proposed algorithm is validated by comparing proposed algorithm with the results of the existing
segmentation algorithm used for the same purpose.
Keywords: segmentation; clustering; k-means clustering; fuzzy c means clustering; mean shift; canny edge
detection; BEA; brain extraction algorithm.
I. Iintroduction
The main reason of image segmentation is to partition an input image into meaningful regions with
respect to our particular application. The major significance of applying segmentation technique is to obtain
coarse and fine details of tissues of brain MRI images in detail (Atkins and Mackiewich, 1998). Image
segmentation is mainly used to detect objects and boundaries like lines, curves, edges etc. Image segmentation is
the process of assigning a label to every pixel in an image which then assigned the same label who shares the
same particular characteristics. The result of image segmentation is a set of segments that collectively cover the
interested region in an image, or a set of contours extracted from the image (Gonzalez and Richard, 2007). Each
of the pixels in a region is similar with respect to some characteristic or computed property, such as color,
intensity, or texture. Adjacent regions are significantly different with respect to the same characteristic. The
success or failure of computerized analysis procedure is specified by segmentation accuracy. The resulting
contours after image segmentation of MRI images can be used to create 3D (Raya, 1990) reconstructions with
the help of interpolation algorithms. Major applications of medical image processing are: detect and locate
tumours and other pathologies issues (Brummer and Mersereau, 1993) measure tissue volumes, diagnosis, study
of anatomical structure, surgery planning, virtual surgery simulation, and intra-surgery navigation.
In general, there are three advantages in image segmentation. The first advantage is the speed of the
algorithm. The segmentation of image should not consume much time. The second advantage is good edge
connectivity of its segmenting result (Perona and Malik, 1990). The third advantage is good shape matching.
Consequently, it will be reliable (Somasundaram and Kalaiselvi, 2011). The limited disadvantages of
segmentations could be fatal problem, the computation time and over segmentation. The result is sensitive to the
selection of the initial random centroids in some of the segmentation techniques. Some region segmentation
techniques can produce blocky segments.
Medical Resonance Image segmentation is very essential feature in most of image processing methods,
which reflects anatomical structure of segment (brain tissue). The usefulness of these methods in clinical
environment significantly depends on the ease of computation and the reduction of human intervention. The
proposed method is based on histogram based gradient calculation, which segments out primary objects from T2
brain MR image (Gilanie and Attique, 2013). The applicability of this algorithm has been practically verified
giving satisfactory results. It is established that the proposed method can be applied on other medical imaging
modalities or other image processing domains and is quite efficient.
2. An effective segmentation technique for Brain Medical Resonance Images
DOI: 10.9790/1676-1103041623 www.iosrjournals.org 17 | Page
II. Proposed Algorithm for Brain Region Extraction
The main purpose of the flow chart shown in figure 1 is to detect the region of brain with many pre-
processing steps. The input MRI image is in JPEG form which selects one of the slices from more than 100
slices of patientâs data. That has to be T2 weighted image for fine analysis of the brain. Before finding tumour it
is important to segment the region of interest. Over here the region of interest is only brain area which has to be
removed from skull. 2D-BEA is one of the techniques which extract the brain area from skull and boundary. In
2D-BEA first of all in the first stage the background noise from the brain image will be removed using low pass
filter and the output is further diffused to enhance the brain boundaries. This will be forwarded by thresholding
in which mask for the coarse brain is generated. In the second stage, morphological based segmentation
operation is performed with connected component analysis to extract the fine brain from the coarse brain portion
obtained in first stage of 2D-BEA.
Figure 1: Proposed technique for Segmentation of brain MRI image
The scalp-skull boundaries are very weak in T2-weighted images (Somasundaram and Kalaiselvi,
2011) and hence they are not preserved. The diffusion process helps to compute an intensity threshold value
automatically to segment the brain from non-brain tissues. Thus stage one is preserving brain borders.
Thresholding helps to produce the rough mask brain. In the second stage main shape characteristics of object
will be identified. Erosion and dilation produced the curve boundaries of brain regions of binary image. As per
the figure 1, the segmented brain region will be further given to 3D-BEA because the 2D-BEA has limitation for
largest connected component identification (Somasundaram and Kalaiselvi, 2011). 3D-BEA may remove that
limitation and could be implemented for further research.
2.1 Implementation of developed segmentation algorithm.
Figure 2 Extracted brain region
3. An effective segmentation technique for Brain Medical Resonance Images
DOI: 10.9790/1676-1103041623 www.iosrjournals.org 18 | Page
Extracted brain region
The results in figure 2 shows that using 2D-BEA method the interested brain region is extracted and it
removes the unwanted area like skull and other boundaries. It is fulfilling the main purpose of finding out the
different possible locations of tumour for the later research.
III. Comparative Analysis Of Developed Algorithm With Different Segmentation Techniques
For MRI Brain Images
In addition to the analysis of developed algorithm, the comparative analysis is also carried out with the
existing algorithms. It would be easy to identify the efficient algorithm after this analysis. In this section total
five segmentation algorithms have been implemented. Mainly cluster based algorithms are compared to validate
developed algorithm. Below shows the algorithms and their steps with their importance in different aspects.
3.1 Threshold based image segmentation
The threshold technique is simplest in segmentation methods. To set two thresholds on the histogram
of the image, we can classify between the two thresholds in the histogram as the same region and classify the
others as the second region (Gonzalez and Richard, 2007).
3.2 Edge based segmentation
This includes detection of edges using Prewitt based, Sobel based, Robert Cross edge, Laplacian edge,
Canny based edge detection. Detected edges in an image are assumed to represent object boundaries, and used
to identify these objects (Fritz, 2010).
Edge detection very seldom gives you the perfect unambiguous and closed boundaries you need for a
direct segmentation. There will frequently be spurious edges detected where they shouldnât be, and gaps occur
where there should be edges. An advanced and versatile technique for clustering-based segmentation is
presented. Below images shows the results of Matlab simulations for different segmentation techniques.
3.3 Fuzzy c means based clustering
One family of segmentation algorithms is based on the idea of clustering pixels with similar
characteristics. Fuzzy c-means has been shown to work well for clustering based segmentation (Benjamin,
2012), however due to its iterative nature this approach has excessive computational requirements. Below shows
the algorithm of Fuzzy c means.
Step 1: Initialize the membership Matrix
Step 2: Calculate the degree of membership
4. An effective segmentation technique for Brain Medical Resonance Images
DOI: 10.9790/1676-1103041623 www.iosrjournals.org 19 | Page
Step 3: Compute the centroid and update the new membership and Recalculate the degree of membership
Step 4: If the difference of centroid matrix between new and previous iteration is less than the predefine value
then recalculate the degree of membership.
3.4 K-means clustering
It is one of the easiest methods of unsupervised learning algorithm that solve the well-known clustering
issue (Funmilola and Adedeji, 2012). K-means is purposed by Macqueen in 1967. K-means is a simple
clustering method which is having low computational complexity as compared to Fuzzy C-means. K-means
clustering do not overlap the clusters.
= (Xk-Ci)) (1)
Below shows the algorithm of k means clustering algorithm for MRI brain image segmentation.
Step 1: Select K points as the initial centroids.
Step 2: Repeat
Step 3: Form K clusters by assigning all points to the closest centroid
Step 4: Recompute the centroid of each cluster.
Step 5: Until the centroids donât change.
Simplicity and easy implementation are some advantages of k-means but it has several drawbacks as
well. There is no standard for a good set of initial centres. Instead of random choices, initial k-means results can
provide the initial points for the next run of the algorithm.
3.5 Mean shift clustering
Mean Shift is defined as finding modes in a set of data samples, manifesting an underlying probability
density function (PDF) (Pilar and Manuel, 2011). Hence the Certain problem occurred in the above techniques
has been solved by Mean Shift. Mean shift clustering is one of the most non-parametric clustering techniques
which do not require any prior knowledge of the clusters.
Kernel density estimation (known as Parzen window technique in pattern recognition) is the most popular
density estimation method. The Parzen-window estimate is defined as
(2)
Where k(x) =window function (Kernel in D-dimensional), hn>0 =Width of the Kernel.
Where k(x) is the window function or kernel in the d-dimensional space such that
(3)
(4)
The algorithm for Mean Shift segmentation is as below.
Step 1: Take an input image and fine each point Xi
Step 2: Choose a search window for finding out maximum dense area.
Step 3: Compute the mean shift vector
Step 4: If Mean shift is Optimum then stop the process or again find new point Xi. There are certain advantages
for mean shift. I.e. good general-practice segmentation, flexible in number and shape of regions, robust to
outliers.
Figure 3 Thresholding based segmentation
5. An effective segmentation technique for Brain Medical Resonance Images
DOI: 10.9790/1676-1103041623 www.iosrjournals.org 20 | Page
Figure 4: Edge based segmentation
Figure 5 K means clustering based segmentation
6. An effective segmentation technique for Brain Medical Resonance Images
DOI: 10.9790/1676-1103041623 www.iosrjournals.org 21 | Page
Figure 6 Fuzzy C Means clustering
Figure 7 Mean Shift clustering
TABLE 1 Comparison of different image segmentation techniques for MRI images of brain
Parameter Mean Shift Fuzzy C-Means K-Means Canny Edge
detector
Thresholding Brain
Extraction
algorithm
2D-BEA
Noise Removing the
noise by
filtering.
Cannot remove
noise
Cannot remove
noise.
Only Detected
the boundary of
that image.
Not involve the
spatial information of
the images, so it will
bring about noise,
blurred edges.
Removing the
noise by
filtering.
Smoothing More
Smoothed
image is
produced.
Doesnât smooth
the image
Different initial
centroids will
bring about the
different results.
Smoothed
Image but Less
Compare to
Mean Shift.
Outlier in the images. More
Smoothed
image is
produced.
Separation Cannot
separate
unwanted area
like skull
Used for MRI
image
segmentation
Other region can
also separate.
Can separate all
edges of
particular
image.
Only separate binary
image.
Can separate
unwanted area
like skull
7. An effective segmentation technique for Brain Medical Resonance Images
DOI: 10.9790/1676-1103041623 www.iosrjournals.org 22 | Page
No. of
Cluster
Does not
require prior
knowledge of
the number of
clusters.
Apriori
Specification of
the number of
cluster
A problem of
choice of
numbers of
cluster N.
- - Does not
require prior
knowledge of
the number of
clusters.
Over
segmentati
on
Guarantees an
over-
segmented
image while
keeping fine
image details.
Does not over
segmented image.
Does not over
segmented
image.
Does not over
segmented
image.
Does not over
segmented image.
Does not over
segmented
image.
Above table 1 depicts the importance of proposed algorithm 2D-BEA in all aspects towards better
segmentation of brain MRI images. As compared to other segmentation algorithms searched by previous
researchers the 2D-BEA algorithm is removing noise from the input image with minimum computational time.
Proposed algorithm is also efficient to identify the smooth edges as well as preserving information. This
developed algorithm does not over segment the image so that blur effect comes down to null. Moreover it is so
user friendly and automated that it does not expense time to diagnose unwanted area and only extract the desire
region of brain MRI images.
IV. Conclusion
Brain extraction algorithm is a general category of algorithms used to extract/evaluate features of a
given brain scan. It also performs the very same task of post processing. In first stage, coarse brain is generated
using filtering and thresholding. In second stage, morphological operations performed on binary image to
segment the fine brain mask. It has been observed from the comparative analysis that existing methodology of
segmentation is not able to remove noise as well as blur the images and moreover it does not extract brain area
which is the main interest for further research and for finding out the tumour in the later stage. In future to
overcome the failure of 2D-BEA, 3D-BEA will be used. For the current research the partial algorithm of
proposed technique has been implemented. Further study will be taken up in near future for image classification
and other pre-processing. Results show the validity of the 2D-BEA algorithm and its advantages through
comparison.
Acknowledgements
The authors would like to acknowledge the motivation and support given by radiologists and doctors
from different MRI centers for providing the Brain MRI images and the corresponding information for the
research.
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Biographical notes: Vandana Shah received her master research in VLSI technology from Nagpur University,
India in 2012. She is currently a researcher in medical field for MRI brain tumour analysis and pursuing her
PhD in Department of Electronics from the Nagpur university, her current research interest include, the
computer assisted brain anatomy analysis and image segmentation techniques. Vijay S. Chourasia is working as
an Assistant Professor in the Department of Electronics and Communication Engineering at Manoharbhai Patel
Institute of Engineering and Technology, Gondia, India. He obtained his PhD from The LNM Institute of
Information Technology, Jaipur. He has more than 25 years experience in the field of academics and has about
35 research publications in various international and national journals and conferences. His research interest is
Signal and Image Processing. Ravindra V. Kshirsagar is working as a professor in the Department of
Electronics Engineering at Priyadarshini College of Engineering (PCE) and Vice Principal at PCE, Nagpur. He
is also the former-dean of Nagpur University. He obtained his Ph.D. from VNIT, Nagpur in septâ2010. He has a
vast teaching experience of 27 years and 2 years of industry experience. He has published many research papers
in national and international conferences. His special field of interest includes Reconfigurable Computing, VLSI
Design, Fault tolerance and DFT.