In this paper, we present a novel method for image segmentation of the hip joint structure. The
key idea is to transfer the ground truth segmentation from the database to the test image. The
ground truth segmentation of MR images is done by medical experts. The process includes the
top down approach which register the shape of the test image globally and locally with the
database of train images. The goal of top down approach is to find the best train image for each
of the local test image parts. The bottom up approach replaces the local test parts by best train
image parts, and inverse transform the best train image parts to represent a test image by the
mosaic of best train image parts. The ground truth segmentation is transferred from best train
image parts to their corresponding location in the test image.
Segmentation of Tumor Region in MRI Images of Brain using Mathematical Morpho...CSCJournals
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.
MRIIMAGE SEGMENTATION USING LEVEL SET METHOD AND IMPLEMENT AN MEDICAL DIAGNOS...cseij
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
Brain tumor detection and segmentation using watershed segmentation and morph...eSAT Journals
This document describes a method for detecting and segmenting brain tumors from MRI images using watershed segmentation and morphological operations. The method involves preprocessing the MRI image, removing the skull via thresholding, segmenting the brain tissue using marker-controlled watershed segmentation, detecting the tumor region using erosion-based morphological operations, calculating the tumor area, and determining the tumor location. The method was implemented in MATLAB and experimental results demonstrated that it could accurately extract and detect tumor regions from brain MRI images.
Medical Image Segmentation Based on Level Set MethodIOSR Journals
This document presents a new medical image segmentation technique based on the level set method. The technique uses a combination of thresholding, morphological erosion, and a variational level set method. Thresholding is applied to determine object pixels, followed by optional erosion to remove small fragments. Then a variational level set method is applied on the original image to evaluate the contour and segment objects. The technique is tested on various medical images and provides good segmentation results, though it struggles with complex images containing multiple distinct objects.
Literature Survey on Detection of Brain Tumor from MRI Images IOSR Journals
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.
MEDICAL IMAGE TEXTURE SEGMENTATION USINGRANGE FILTERcscpconf
Medical image segmentation is a frequent processing step in image understanding and computer
aided diagnosis. In this paper, we propose medical image texture segmentation using texture
filter. Three different image enhancement techniques are utilized to remove strong speckle noise as well enhance the weak boundaries of medical images. We propose to exploit the concept of range filtering to extract the texture content of medical image. Experiment is conducted on ImageCLEF2010 database. Results show the efficacy of our proposed medical image texture segmentation.
Automatic detection of optic disc and blood vessels from retinal images using...eSAT Journals
Abstract Diabetic retinopathy is the common cause of blindness. This paper presents the mathematical morphology method to detect and eliminate the optic disc (OD) and the blood vessels. Detection of optic disc and the blood vessels are the necessary steps in the detection of diabetic retinopathy because the blood vessels and the optic disc are the normal features of the retinal image. And also, the optic disc and the exudates are the brightest portion of the image. Detection of optic disc and the blood vessels can help the ophthalmologists to detect the diseases earlier and faster. Optic disc and the blood vessels are detected and eliminated by using mathematical morphology methods such as closing, filling, morphological reconstruction and Otsu algorithm. The objective of this paper is to detect the normal features of the image. By using the result, the ophthalmologists can detect the diseases easily. Keywords: Blood vessels, Diabetic retinopathy, mathematical morphology, Otsu algorithm, optic disc (OD)
Adaptive K-Means Clustering Algorithm for MR Breast Image Segmentation
3D Brain Tumor Segmentation Scheme using K-mean Clustering and Connected Component Labeling Algorithms
Volume Identification and Estimation of MRI Brain Tumor
MRI Breast cancer diagnosis hybrid approach using adaptive Ant-based segmentation and Multilayer Perceptron NN classifier
Segmentation of Tumor Region in MRI Images of Brain using Mathematical Morpho...CSCJournals
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.
MRIIMAGE SEGMENTATION USING LEVEL SET METHOD AND IMPLEMENT AN MEDICAL DIAGNOS...cseij
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
Brain tumor detection and segmentation using watershed segmentation and morph...eSAT Journals
This document describes a method for detecting and segmenting brain tumors from MRI images using watershed segmentation and morphological operations. The method involves preprocessing the MRI image, removing the skull via thresholding, segmenting the brain tissue using marker-controlled watershed segmentation, detecting the tumor region using erosion-based morphological operations, calculating the tumor area, and determining the tumor location. The method was implemented in MATLAB and experimental results demonstrated that it could accurately extract and detect tumor regions from brain MRI images.
Medical Image Segmentation Based on Level Set MethodIOSR Journals
This document presents a new medical image segmentation technique based on the level set method. The technique uses a combination of thresholding, morphological erosion, and a variational level set method. Thresholding is applied to determine object pixels, followed by optional erosion to remove small fragments. Then a variational level set method is applied on the original image to evaluate the contour and segment objects. The technique is tested on various medical images and provides good segmentation results, though it struggles with complex images containing multiple distinct objects.
Literature Survey on Detection of Brain Tumor from MRI Images IOSR Journals
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.
MEDICAL IMAGE TEXTURE SEGMENTATION USINGRANGE FILTERcscpconf
Medical image segmentation is a frequent processing step in image understanding and computer
aided diagnosis. In this paper, we propose medical image texture segmentation using texture
filter. Three different image enhancement techniques are utilized to remove strong speckle noise as well enhance the weak boundaries of medical images. We propose to exploit the concept of range filtering to extract the texture content of medical image. Experiment is conducted on ImageCLEF2010 database. Results show the efficacy of our proposed medical image texture segmentation.
Automatic detection of optic disc and blood vessels from retinal images using...eSAT Journals
Abstract Diabetic retinopathy is the common cause of blindness. This paper presents the mathematical morphology method to detect and eliminate the optic disc (OD) and the blood vessels. Detection of optic disc and the blood vessels are the necessary steps in the detection of diabetic retinopathy because the blood vessels and the optic disc are the normal features of the retinal image. And also, the optic disc and the exudates are the brightest portion of the image. Detection of optic disc and the blood vessels can help the ophthalmologists to detect the diseases earlier and faster. Optic disc and the blood vessels are detected and eliminated by using mathematical morphology methods such as closing, filling, morphological reconstruction and Otsu algorithm. The objective of this paper is to detect the normal features of the image. By using the result, the ophthalmologists can detect the diseases easily. Keywords: Blood vessels, Diabetic retinopathy, mathematical morphology, Otsu algorithm, optic disc (OD)
Adaptive K-Means Clustering Algorithm for MR Breast Image Segmentation
3D Brain Tumor Segmentation Scheme using K-mean Clustering and Connected Component Labeling Algorithms
Volume Identification and Estimation of MRI Brain Tumor
MRI Breast cancer diagnosis hybrid approach using adaptive Ant-based segmentation and Multilayer Perceptron NN classifier
Mri image registration based segmentation framework for whole hearteSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Brain Tumor Segmentation and Extraction of MR Images Based on Improved Waters...IOSR Journals
This document summarizes a research paper about segmenting and extracting brain tumors from MR images using an improved watershed transform technique. It first preprocesses the MR images using techniques like edge enhancement to improve image quality. It then applies a marker-controlled watershed segmentation using foreground and background markers to avoid oversegmentation. The watershed transform is further improved by removing noise, adjusting pixel values, and introducing neighborhood relations between boundaries. Finally, mathematical morphology operations like erosion, dilation, opening and closing are used to get clear edges of the extracted brain tumor in the MR image.
Contour evolution method for precise boundary delineation of medical imagesTELKOMNIKA JOURNAL
Image segmentation is an important precursor to boundary delineation of medical images. One of the major challenges in applying automatic image segmentation in medical images is the imperfection in the imaging process which can result in inconsistent contrast and brightness levels, and low image sharpness and vanishing boundaries. Although recent advances in deep learning produce vast improvements in the quality of image segmentation, the accuracy of segmentation around object boundaries still requires improvement. We developed a new approach to contour evolution that is more intuitive but shares some common principles with the active contour model method. The method uses two concepts, namely the boundary grid and sparse boundary representation, as an implicit and explicit representation of the boundary points. We tested our method using lumbar spine MRI images of 515 patients. The experiment results show that our method performs up to 10.2 times faster and more flexible than the geodesic active contours method. Using BF-score contour-based metric, we show that our method improves the boundary accuracy from 74% to 84% as opposed to 63% by the latter method.
Mri brain tumour detection by histogram and segmentationiaemedu
This document summarizes a research paper on detecting brain tumors in MRI images using a combination of histogram thresholding, modified gradient vector field (GVF), and morphological operators. The non-brain regions are removed using morphological operators. Histogram thresholding is then used to detect if the brain is normal or abnormal/contains a tumor. If abnormal, the modified GVF is used to detect the tumor contour. The proposed method aims to be computationally efficient by only performing segmentation if a tumor is detected. It was tested on many MRI brain images and performance was validated against human expert segmentation.
International Journal of Computational Engineering Research(IJCER)ijceronline
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.
Clustering of medline documents using semi supervised spectral clusteringeSAT Journals
Abstract We are considering: local-content (LC) information, global-content (GC) information from PubMed and MESH (medical subject heading-MS) for the clustering of bio-medical documents. The performances of MEDLINE document clustering are enhanced from previous methods by combining both the LC and GC. We propose a semi-supervised spectral clustering method to overcome the limitations of representation space of earlier methods. Keywords- document clustering, semi-supervised clustering, spectral clustering
IRJET - A Review on Segmentation of Chest RadiographsIRJET Journal
This document reviews and compares various techniques for segmenting anatomical structures from chest radiographs. It begins with an introduction to image segmentation and its importance in medical imaging. It then describes 12 different segmentation methods that have been used for segmenting lungs and other structures from chest radiographs, including active shape models, active appearance models, pixel classification, visual saliency, convolutional neural networks, and others. For each method, it provides details on the algorithm and compares their performance based on accuracy, sensitivity and specificity. In conclusion, it discusses some of the challenges of medical image segmentation and suggests that hybrid approaches combining multiple techniques may be most effective.
Medical Image segmentation using Image Mining conceptsEditor IJMTER
Image differencing is usually done by subtracting the low-level skin texture like strength
in images that are already associated. This paper extracts high-level skin texture in order to find out
an efficient image differencing method for the analysis of Brain Tumor. On the other hand, this
produces sets of skin texture that are both spatial. We demonstrate a technique that avoids arbitrary
spatial constraints and is robust in the presence of sound, outliers, and imaging artifact, while
outperforming even profitable products in the analysis of Brain Tumor images. First, the landmark
are establish, and then the top entrant are sorted into a end set. Second, the top sets of the two
descriptions are then differenced through a cluster judgment. The symmetry of the human body is
utilized to increase the accuracy of the finding. We imitate this technique in an effort to understand
and ultimately capture the judgment of the radiologist. The image differencing with clustered
contrast process determines the being there of Brain Tumor. Using the most favorable features
extracted from normal and tumor regions of MRI by using arithmetical features, classifiers are used
to categorize and segment the tumor portion in irregular images. Both the difficult and preparation
phase gives the proportion of accuracy on each parameter in neural networks, which gives the idea to
decide the best one to be used in supplementary works. The results showed outperformance of
algorithm when compared with classification accuracy which works as shows potential tool for
classification and requires extension in brain tumor analysis.
Comparative performance analysis of segmentation techniquesIAEME Publication
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.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Literature survey for 3 d reconstruction of brain mri imageseSAT Journals
Abstract
Since Doctors had only the 2D Image Data to visualize the tumors in the MRI images, which never gave the actual feel of how the tumor would exactly look like . The doctors were deprived from the exact visualization of the tumor the amount of the tumor to be removed by operation was not known, which caused a lot of deformation in the faces and structure of the patients face or skull. The diversity and complexity of tumor cells makes it very challenging to visualize tumor present in magnetic resonance image (MRI) data. Hence to visualize the tumor properly 2D MRI image has to be converted to 3D image. With the development of computer image processing technology, three-dimensional (3D) visualization has become an important method of the medical diagnose, it offers abundant and accurate information for medical experts. Three-dimensional (3-D) reconstruction of medical images is widely applied to tumor localization; surgical planning and brain electromagnetic field computation etc. The brain MR images have unique characteristics, i.e., very complicated changes of the gray-scales and highly irregular boundaries. Traditional 3-D reconstruction algorithms are challenged in solving this problem. Many reconstruction algorithms, such as marching cubes and dividing cubes, need to establish the topological relationship between the slices of images. The results of these traditional approaches vary depending on the number of input sections, their positions, the shape of the original body and the applied interpolation technique. These make the task tedious and time-consuming. Moreover, satisfied reconstruction result may not even be obtained when the highly irregular objects such as the encephalic tissues are considered. Due to complexity and irregularity of each encephalic tissue boundary, three-dimensional (3D) reconstruction for MRI image is necessary. A Literature survey is done to study different methods of 3D reconstruction of brain images from MRI images. Keywords: 3-D reconstruction, region growing, segmentation method, immune algorithm (IA), one class support vector machine (OCSVM) and sphere shaped support vector machine (SSSVM).
Learning Based Single Frame Image Super-resolution Using Fast Discrete Curvel...CSCJournals
High-resolution (HR) images play a vital role in all imaging applications as they offer more details. The images captured by the camera system are of degraded quality due to the imaging system and are low-resolution (LR) images. Image super-resolution (SR) is a process, where HR image is obtained from combining one or multiple LR images of same scene. In this paper, learning based single frame image super-resolution technique is proposed by using Fast Discrete Curvelet Transform (FDCT) coefficients. FDCT is an extension to Cartesian wavelets having anisotropic scaling with many directions and positions, which forms tight wedges. Such wedges allow FDCT to capture the smooth curves and fine edges at multiresolution level. The finer scale curvelet coefficients of LR image are learnt locally from a set of high-resolution training images. The super-resolved image is reconstructed by inverse Fast Discrete Curvelet Transform (IFDCT). This technique represents fine edges of reconstructed HR image by extrapolating the FDCT coefficients from the high-resolution training images. Experimentation based results show appropriate improvements in MSE and PSNR.
Brain tumor detection and segmentation using watershed segmentation and morph...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
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.
This document proposes a novel multi-region segmentation method to extract myocardial scar tissue from late-enhancement cardiac MRI images. It introduces a partially-ordered Potts model to encode the spatial relationships between cardiac regions, and solves the associated optimization problem using convex relaxation and a hierarchical continuous max-flow formulation. Experiments on 50 whole heart 3D MRI datasets demonstrate the method can accurately segment scar tissue without requiring prior myocardial segmentation, and with substantially reduced processing time compared to conventional methods.
Brain Tumor Extraction from T1- Weighted MRI using Co-clustering and Level Se...CSCJournals
The aim of the paper is to propose effective technique for tumor extraction from T1-weighted magnetic resonance brain images with combination of co-clustering and level set methods. The co-clustering is the effective region based segmentation technique for the brain tumor extraction but have a drawback at the boundary of tumors. While, the level set without re-initialization which is good edge based segmentation technique but have some drawbacks in providing initial contour. Therefore, in this paper the region based co-clustering and edge-based level set method are combined through initially extracting tumor using co-clustering and then providing the initial contour to level set method, which help in cancelling the drawbacks of co-clustering and level set method. The data set of five patients, where one slice is selected from each data set is used to analyze the performance of the proposed method. The quality metrics analysis of the proposed method is proved much better as compared to level set without re-initialization method.
Survey on Brain MRI Segmentation TechniquesEditor IJMTER
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.
INVESTIGATION THE EFFECT OF USING GRAY LEVEL AND RGB CHANNELS ON BRAIN TUMOR ...csandit
Analysis the effect of using gray level on the Brain tumor image for improving speed of object
detection in the field of Medical Image using image processing technique. Specific areas of
interest are image binarization method, Image segmentation. Experiments will be performed by
image processing using Matlab. This paper presents a strategy for decreasing the calculation
time by using gray level and just one channel Red or Green or Blue in medical Image and
analysis its impact in order to improve detection time and the main goal is to reduce time
complexity.
Writing skills are abilities that help writers communicate their thoughts in a meaningful way. The document provides five tips for developing good writing skills: focus on the main message, begin with a mind map, guess words from context, give constructive feedback, and integrate skills. Mastering writing skills allows learners to write independently, clearly, fluently, and creatively so that others can understand what they have written.
Literacy Ass1A: Handwriting and PunctuationJasmineMurphy
This document summarizes an assignment submitted by Sania Bahrom and Jasmine Murphy on the topic of handwriting and punctuation for their Bachelor of Education course. The assignment details include the topic, which is a presentation on handwriting and punctuation, and is due on March 30th, 2012. The document also includes a declaration signed by Sania and Jasmine, certifying that the work is original.
Mri image registration based segmentation framework for whole hearteSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Brain Tumor Segmentation and Extraction of MR Images Based on Improved Waters...IOSR Journals
This document summarizes a research paper about segmenting and extracting brain tumors from MR images using an improved watershed transform technique. It first preprocesses the MR images using techniques like edge enhancement to improve image quality. It then applies a marker-controlled watershed segmentation using foreground and background markers to avoid oversegmentation. The watershed transform is further improved by removing noise, adjusting pixel values, and introducing neighborhood relations between boundaries. Finally, mathematical morphology operations like erosion, dilation, opening and closing are used to get clear edges of the extracted brain tumor in the MR image.
Contour evolution method for precise boundary delineation of medical imagesTELKOMNIKA JOURNAL
Image segmentation is an important precursor to boundary delineation of medical images. One of the major challenges in applying automatic image segmentation in medical images is the imperfection in the imaging process which can result in inconsistent contrast and brightness levels, and low image sharpness and vanishing boundaries. Although recent advances in deep learning produce vast improvements in the quality of image segmentation, the accuracy of segmentation around object boundaries still requires improvement. We developed a new approach to contour evolution that is more intuitive but shares some common principles with the active contour model method. The method uses two concepts, namely the boundary grid and sparse boundary representation, as an implicit and explicit representation of the boundary points. We tested our method using lumbar spine MRI images of 515 patients. The experiment results show that our method performs up to 10.2 times faster and more flexible than the geodesic active contours method. Using BF-score contour-based metric, we show that our method improves the boundary accuracy from 74% to 84% as opposed to 63% by the latter method.
Mri brain tumour detection by histogram and segmentationiaemedu
This document summarizes a research paper on detecting brain tumors in MRI images using a combination of histogram thresholding, modified gradient vector field (GVF), and morphological operators. The non-brain regions are removed using morphological operators. Histogram thresholding is then used to detect if the brain is normal or abnormal/contains a tumor. If abnormal, the modified GVF is used to detect the tumor contour. The proposed method aims to be computationally efficient by only performing segmentation if a tumor is detected. It was tested on many MRI brain images and performance was validated against human expert segmentation.
International Journal of Computational Engineering Research(IJCER)ijceronline
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.
Clustering of medline documents using semi supervised spectral clusteringeSAT Journals
Abstract We are considering: local-content (LC) information, global-content (GC) information from PubMed and MESH (medical subject heading-MS) for the clustering of bio-medical documents. The performances of MEDLINE document clustering are enhanced from previous methods by combining both the LC and GC. We propose a semi-supervised spectral clustering method to overcome the limitations of representation space of earlier methods. Keywords- document clustering, semi-supervised clustering, spectral clustering
IRJET - A Review on Segmentation of Chest RadiographsIRJET Journal
This document reviews and compares various techniques for segmenting anatomical structures from chest radiographs. It begins with an introduction to image segmentation and its importance in medical imaging. It then describes 12 different segmentation methods that have been used for segmenting lungs and other structures from chest radiographs, including active shape models, active appearance models, pixel classification, visual saliency, convolutional neural networks, and others. For each method, it provides details on the algorithm and compares their performance based on accuracy, sensitivity and specificity. In conclusion, it discusses some of the challenges of medical image segmentation and suggests that hybrid approaches combining multiple techniques may be most effective.
Medical Image segmentation using Image Mining conceptsEditor IJMTER
Image differencing is usually done by subtracting the low-level skin texture like strength
in images that are already associated. This paper extracts high-level skin texture in order to find out
an efficient image differencing method for the analysis of Brain Tumor. On the other hand, this
produces sets of skin texture that are both spatial. We demonstrate a technique that avoids arbitrary
spatial constraints and is robust in the presence of sound, outliers, and imaging artifact, while
outperforming even profitable products in the analysis of Brain Tumor images. First, the landmark
are establish, and then the top entrant are sorted into a end set. Second, the top sets of the two
descriptions are then differenced through a cluster judgment. The symmetry of the human body is
utilized to increase the accuracy of the finding. We imitate this technique in an effort to understand
and ultimately capture the judgment of the radiologist. The image differencing with clustered
contrast process determines the being there of Brain Tumor. Using the most favorable features
extracted from normal and tumor regions of MRI by using arithmetical features, classifiers are used
to categorize and segment the tumor portion in irregular images. Both the difficult and preparation
phase gives the proportion of accuracy on each parameter in neural networks, which gives the idea to
decide the best one to be used in supplementary works. The results showed outperformance of
algorithm when compared with classification accuracy which works as shows potential tool for
classification and requires extension in brain tumor analysis.
Comparative performance analysis of segmentation techniquesIAEME Publication
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.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Literature survey for 3 d reconstruction of brain mri imageseSAT Journals
Abstract
Since Doctors had only the 2D Image Data to visualize the tumors in the MRI images, which never gave the actual feel of how the tumor would exactly look like . The doctors were deprived from the exact visualization of the tumor the amount of the tumor to be removed by operation was not known, which caused a lot of deformation in the faces and structure of the patients face or skull. The diversity and complexity of tumor cells makes it very challenging to visualize tumor present in magnetic resonance image (MRI) data. Hence to visualize the tumor properly 2D MRI image has to be converted to 3D image. With the development of computer image processing technology, three-dimensional (3D) visualization has become an important method of the medical diagnose, it offers abundant and accurate information for medical experts. Three-dimensional (3-D) reconstruction of medical images is widely applied to tumor localization; surgical planning and brain electromagnetic field computation etc. The brain MR images have unique characteristics, i.e., very complicated changes of the gray-scales and highly irregular boundaries. Traditional 3-D reconstruction algorithms are challenged in solving this problem. Many reconstruction algorithms, such as marching cubes and dividing cubes, need to establish the topological relationship between the slices of images. The results of these traditional approaches vary depending on the number of input sections, their positions, the shape of the original body and the applied interpolation technique. These make the task tedious and time-consuming. Moreover, satisfied reconstruction result may not even be obtained when the highly irregular objects such as the encephalic tissues are considered. Due to complexity and irregularity of each encephalic tissue boundary, three-dimensional (3D) reconstruction for MRI image is necessary. A Literature survey is done to study different methods of 3D reconstruction of brain images from MRI images. Keywords: 3-D reconstruction, region growing, segmentation method, immune algorithm (IA), one class support vector machine (OCSVM) and sphere shaped support vector machine (SSSVM).
Learning Based Single Frame Image Super-resolution Using Fast Discrete Curvel...CSCJournals
High-resolution (HR) images play a vital role in all imaging applications as they offer more details. The images captured by the camera system are of degraded quality due to the imaging system and are low-resolution (LR) images. Image super-resolution (SR) is a process, where HR image is obtained from combining one or multiple LR images of same scene. In this paper, learning based single frame image super-resolution technique is proposed by using Fast Discrete Curvelet Transform (FDCT) coefficients. FDCT is an extension to Cartesian wavelets having anisotropic scaling with many directions and positions, which forms tight wedges. Such wedges allow FDCT to capture the smooth curves and fine edges at multiresolution level. The finer scale curvelet coefficients of LR image are learnt locally from a set of high-resolution training images. The super-resolved image is reconstructed by inverse Fast Discrete Curvelet Transform (IFDCT). This technique represents fine edges of reconstructed HR image by extrapolating the FDCT coefficients from the high-resolution training images. Experimentation based results show appropriate improvements in MSE and PSNR.
Brain tumor detection and segmentation using watershed segmentation and morph...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
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.
This document proposes a novel multi-region segmentation method to extract myocardial scar tissue from late-enhancement cardiac MRI images. It introduces a partially-ordered Potts model to encode the spatial relationships between cardiac regions, and solves the associated optimization problem using convex relaxation and a hierarchical continuous max-flow formulation. Experiments on 50 whole heart 3D MRI datasets demonstrate the method can accurately segment scar tissue without requiring prior myocardial segmentation, and with substantially reduced processing time compared to conventional methods.
Brain Tumor Extraction from T1- Weighted MRI using Co-clustering and Level Se...CSCJournals
The aim of the paper is to propose effective technique for tumor extraction from T1-weighted magnetic resonance brain images with combination of co-clustering and level set methods. The co-clustering is the effective region based segmentation technique for the brain tumor extraction but have a drawback at the boundary of tumors. While, the level set without re-initialization which is good edge based segmentation technique but have some drawbacks in providing initial contour. Therefore, in this paper the region based co-clustering and edge-based level set method are combined through initially extracting tumor using co-clustering and then providing the initial contour to level set method, which help in cancelling the drawbacks of co-clustering and level set method. The data set of five patients, where one slice is selected from each data set is used to analyze the performance of the proposed method. The quality metrics analysis of the proposed method is proved much better as compared to level set without re-initialization method.
Survey on Brain MRI Segmentation TechniquesEditor IJMTER
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.
INVESTIGATION THE EFFECT OF USING GRAY LEVEL AND RGB CHANNELS ON BRAIN TUMOR ...csandit
Analysis the effect of using gray level on the Brain tumor image for improving speed of object
detection in the field of Medical Image using image processing technique. Specific areas of
interest are image binarization method, Image segmentation. Experiments will be performed by
image processing using Matlab. This paper presents a strategy for decreasing the calculation
time by using gray level and just one channel Red or Green or Blue in medical Image and
analysis its impact in order to improve detection time and the main goal is to reduce time
complexity.
Writing skills are abilities that help writers communicate their thoughts in a meaningful way. The document provides five tips for developing good writing skills: focus on the main message, begin with a mind map, guess words from context, give constructive feedback, and integrate skills. Mastering writing skills allows learners to write independently, clearly, fluently, and creatively so that others can understand what they have written.
Literacy Ass1A: Handwriting and PunctuationJasmineMurphy
This document summarizes an assignment submitted by Sania Bahrom and Jasmine Murphy on the topic of handwriting and punctuation for their Bachelor of Education course. The assignment details include the topic, which is a presentation on handwriting and punctuation, and is due on March 30th, 2012. The document also includes a declaration signed by Sania and Jasmine, certifying that the work is original.
The document discusses various aspects of the writing process including the stages of writing (prewriting, drafting, revising, editing, and publishing), levels of support a teacher can provide, approaches to writing instruction (writing workshop and thematic), strategies writers use, skills writers need, strategies for informal writing, and common writing genres.
This document discusses Croatia's approach to integrating information and communication technologies (ICT) into its national school curriculum. It describes a bottom-up and top-down approach. The bottom-up approach involves getting individual teachers interested and trained in using ICT, while the top-down approach implements ICT on a national scale through policy decisions. The Croatian Academic and Research Network (CARNet) has played a key role in both approaches through various projects to provide infrastructure, training, and support to educational institutions. The ultimate goal is to transform schools into "digitally mature" environments that can fully utilize and benefit from ICT.
The document discusses the importance of developing strong writing skills, especially for business communication. It covers various types of business writing like emails, reports, letters, memos, and promotional materials. The document provides tips for effective business writing, such as planning, following guidelines for grammar and spelling, being concise and clear, and proofreading work. It also discusses challenges of email communication and offers suggestions for improving writing style and quality in business documents.
This document provides a compilation of top-down, bottom-up, and metacognitive techniques for developing listening and reading skills in English 17 (The Teaching of Listening and Reading). It defines top-down processing as using background knowledge to predict meaning, and provides examples of top-down techniques like predicting, inferring, and summarizing. Specific listening activities that encourage top-down processing are described, such as using pictures to sequence events or identifying locations of conversations. The document also lists techniques for activating students' prior knowledge, such as word association tasks, to help with comprehension.
This document discusses various approaches to teaching writing skills. It covers topics like the writing process, genres, creative writing, and writing as a cooperative activity. It emphasizes that teaching writing involves considering skills like handwriting, spelling, layout, and punctuation. The teacher plays important roles as a motivator, providing resources and feedback. Different writing activities can build writing habits or practice specific skills. Portfolios and journals are also discussed. The conclusion stresses that teaching writing requires considering many factors.
OT for Kids - Assessing and Improving Handwriting for Occupational Therapists...Nathan Varma
OT for Kids are a private paediatric Occupational Therapy company. We reegulalry run courses, and on saturday 16th August we ran a handwriting course for Occupational Therapists. This course was a great opportunity to learn more about writing and how Occupational Therapy can assess, improve and treat children with handwriting difficulties.
These slides are for both those people who attended the course and for any Slideshare users who are interested in learning more about OT for Kids, Occupational Therapy and handwriting.
Bottom-up and top-down models describe two approaches to reading. Bottom-up processing focuses on individual letters and words and proceeds from parts to the whole, like the phonics approach which teaches letter-sound relationships. Top-down processing emphasizes using context and prior knowledge to understand texts as a whole before analyzing individual parts, like the whole language approach. Both approaches have benefits for different types of learners.
This document discusses the development of fine motor skills needed for writing. It explains that hands, forearms, fingers and the brain must work together. The hands and fingers provide strength and dexterity while gripping writing tools. Forearms provide stability. Coordination between hands, fingers and eyes is important. Activities are suggested to encourage development like playing with beads, balls and play dough. Stages of early writing development are outlined from scribbling to forming letters. Developing fine motor skills is important for a child's ability to write.
This document summarizes Jeremy Harmer's book "How to Teach English" and provides guidance on teaching writing. It discusses that writing is important for language development and reinforces visual and mental skills. Teachers should consider students' interests and abilities when assigning writing tasks. Writing sequences should start simply and become more complex, from elementary to advanced levels. Teachers are advised to correct writing by having students self-correct or using symbols rather than red ink, and to accommodate different handwriting styles when possible. The document also notes that writing fits well into the "Engage, Study, Activate" teaching model and can promote literature and journaling.
Writing can be classified into different types and modes according to its purpose and form. The types discussed are extensive writing, where the writer is given a subject to write about, and intensive writing, where the focus is on a specific point.
The writing process involves several steps: prewriting to explore the topic, drafting a rough version, revising to improve content and structure, editing for grammar and mechanics, and publishing the final work. Developing writing skills is important for school and career success.
Effective writing has clear ideas and organization, an engaging voice, precise word choice, varied sentence structure, and follows conventions of spelling and grammar. Various techniques can help students improve their writing, such as guided paraphrasing,
Starbucks China 2012: Overview, Analysis & RecommendationsChris Lopez
Starbucks entered the Chinese market in 1999 and has since expanded to over 738 stores in mainland China. It has the largest market share of any coffee shop brand in China at 66%. While coffee consumption in China is still relatively low compared to other countries, it is growing significantly each year as China's economy grows and a larger middle class emerges. Starbucks tailors its offerings to Chinese preferences by providing sweeter coffee drinks and more seating space for socializing over coffee.
I teach a weekly online class to virtual students in my school, focusing on 5th grade Writing Strategies. This PowerPoint is one of the lessons that I designed for the class. It is aligned to CA standards for fifth grade, and also aligned to the K12 curriculum so that students can get guided instruction on one of their lessons and be able to mark it off at the end.
The document discusses good essay writing and provides tips for writing a successful essay. It notes that a good essay must be supported by evidence from reference materials and discussions. It should have an engaging introduction and use clear, simple language and short sentences. The key points should be well organized into coherent paragraphs. Writers are advised to choose an interesting topic they have background knowledge in and know their readers. They should write a draft outline and ensure they have sufficient references before beginning to write their essay.
The document provides guidance on teaching writing skills to students. It discusses the needs for developing writing abilities, such as for academic study and examinations. It then offers advice for teachers on how to structure writing courses, including setting writing tasks, collecting assignments, and providing feedback. The document outlines stages of the writing process like planning, drafting, revising, and editing. It also contrasts traditional and creative approaches to teaching writing and provides examples of classroom activities that can help develop students' writing skills.
The document discusses teaching writing and the six-trait writing model. It introduces the six traits of writing - ideas, organization, voice, word choice, sentence fluency, and conventions. It provides guidelines for teaching writing, including using samples, agreeing on assessment criteria, and using interesting writing prompts. It also includes writing checklists and sample writing prompts.
OT Workshop for pre-K and K teachers. Importance of fine motor skills development and pencil grasp in order to facilitate handwriting success in young children.
This is my slide deck from my session at the North Carolina Reading Conference last week in Raleigh, NC. I do staff development to schools and districts all over the country about best practices in literacy instruction. This topic is one of my most requested.
MRI Image Segmentation Using Level Set Method and Implement an Medical Diagno...CSEIJJournal
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.
This paper proposes an improved watershed transform technique for brain tumor segmentation and extraction from MR images. The technique involves preprocessing images using sobel edge detection, applying an improved watershed transform algorithm that uses marker-controlled segmentation to address oversegmentation issues, and morphological operations. Experimental results on 120 tumor images show the technique successfully extracts tumors with 98% accuracy, outperforming an existing watershed-based method. The extracted tumors provide doctors with important information to aid in diagnosis and treatment planning.
Presentation on deformable model for medical image segmentationSubhash Basistha
Introduction to Image Processing
Steps of Image Processing
Types of Image Processing
Introduction to Image Segmentation
Introduction to Medical Image Segmentation
Application of Image Segmentation
Example of Image Segmentation
Need for Deformable Model
What is Deformable Model??
Types of Deformable Model
An algorithm to quantify the swelling by reconstructing 3D model of the face with stereo images is presented. We
analyzed the primary problems in computational stereo, which include correspondence and depth calculation. Work has been carried out to determine suitable methods for depth estimation and standardizing volume estimations. Finally we designed software for reconstructing 3D images from 2D stereo images, which was built on Matlab and Visual C++. Utilizing
techniques from multi-view geometry, a 3D model of the face was constructed and refined. An explicit analysis of the stereo
disparity calculation methods and filter elimination disparity estimation for increasing reliability of the disparity map was
used. Minimizing variability in position by using more precise positioning techniques and resources will increase the accuracy of this technique and is a focus for future work
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.
Image Segmentation Based Survey on the Lung Cancer MRI ImagesIIRindia
Educational data mining (EDM) creates high impact in the field of academic domain. The methods used in this topic are playing a major advanced key role in increasing knowledge among students. EDM explores and gives ideas in understanding behavioral patterns of students to choose a correct path for choosing their carrier. This survey focuses on such category and it discusses on various techniques involved in making educational data mining for their knowledge improvement. Also, it discusses about different types of EDM tools and techniques in this article. Among the different tools and techniques, best categories are suggested for real world usage.
Image fusion is a technique of
intertwining at least two pictures of same scene to
shape single melded picture which shows indispensable
data in the melded picture. Picture combination
system is utilized for expelling clamor from the
pictures. Commotion is an undesirable material which
crumbles the nature of a picture influencing the
lucidity of a picture. Clamor can be of different kinds,
for example, Gaussian commotion, motivation clamor,
uniform commotion and so forth. Pictures degenerate
some of the time amid securing or transmission or
because of blame memory areas in the equipment.
Picture combination should be possible at three
dimensions, for example, pixel level combination,
highlight level combination and choice dimension
combination. There are essentially two kinds of picture
combination methods which are spatial area
combination systems and transient space combination
procedures. (PCA) combination, Normal strategy, high
pass sifting are spatial area techniques and strategies
which incorporate change, for example, Discrete
Cosine Transform, Discrete wavelet change are
transient space combination strategies. There are
different techniques for picture combination which
have numerous favorable circumstances and
detriments. Numerous procedures experience the ill
effects of the issue of shading curios that comes in the
intertwined picture shaped. Also, the Cyclopean One
of the most astonishing properties of human stereo
vision is the combination of the left and right
perspectives of a scene into a solitary cyclopean one.
Under typical survey conditions, the world shows up as
observed from a virtual eye set halfway between the
left and right eye positions. The apparent picture of
the world is never recorded specifically by any tangible
exhibit, however developed by our neural equipment.
The term cyclopean alludes to a type of visual
upgrades that is characterized by binocular
dissimilarity alone. He suspected that stereo-psis may
find concealed articles, this may be helpful to discover
disguised items. The critical part of this examination
when utilizing arbitrary dab stereo-grams was that
uniqueness is adequate for stereo-psis, and where had
just demonstrated that binocular difference was vital
for stereo-psis.
Face Recognition based on STWT and DTCWT using two dimensional Q-shift Filters IJERA Editor
The Biometrics is used to recognize a person effectively compared to traditional methods of identification. In this paper, we propose a Face recognition based on Single Tree Wavelet Transform (STWT) and Dual Tree Complex Wavelet transform (DTCWT). The Face Images are preprocessed to enhance quality of the image and resize. DTCWT and STWT are applied on face images to extract features. The Euclidian distance is used to compare features of database image with test face images to compute performance parameters. The performance of STWT is compared with DTCWT. It is observed that the DTCWT gives better results compared to STWT technique.
This document proposes a method for improving medical image registration using mutual information. It aims to address limitations in standard mutual information-based registration when there are local intensity variations. The method incorporates spatial and geometric information by computing mutual information in regions identified by the Harris corner detection operator. These regions have large spatial variations that provide geometric information. The method is tested on synthetic and clinical data, showing improved registration accuracy. It is implemented on a GPU for increased parallel processing efficiency, providing a 4-46% speed improvement over standard registration methods.
Comparison of Three Segmentation Methods for Breast Ultrasound Images Based o...IJECEIAES
Breast cancer is one of the major causes of death among women all over the world. The most frequently used diagnosis tool to detect breast cancer is ultrasound. However, to segment the breast ultrasound images is a difficult thing. Some studies show that the active contour models have been proved to be the most successful methods for medical image segmentation. The level set method is a class of curve evolution methods based on the geometric active contour model. Morphological operation describes a range of image processing technique that deal with the shape of features in an image. Morphological operations are applied to remove imperfections that introduced during segmentation. In this paper, we have evaluated three level set methods that combined with morphological operations to segment the breast lesions. The level set methods that used in our research are the Chan Vese (CV) model, the Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) model and the Distance Regularized Level Set Evolution (DRLSE) model. Furthermore, to evaluate the method, we compared the segmented breast lesion that obtained by each method with the lesion that obtained manually by radiologists. The evaluation is done by four metrics: Dice Similarity Coefficient (DSC), True-Positive Ratio (TPR), TrueNegative Ratio (TNR), and Accuracy (ACC). Our experimental results with 30 breast ultrasound images showed that the C-V model that combined with morphological operations have better performance than the other two methods according to mean value of DSC metrics.
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.
brain tumor detection by thresholding approachSahil Prajapati
This technical paper proposes a method for detecting tumors in MRI brain images using thresholding and morphological operations. The methodology involves preprocessing images using sharpening filters, histogram equalization, and median filtering. Threshold segmentation is then used to create binary images, and morphological operations like erosion and dilation are applied. Finally, tumor regions are extracted using image subtraction, which removes closely packed pixels. The authors found that this approach, combining thresholding with morphological operations and subtraction, was effective at detecting and segmenting tumor regions in MRI brain images.
Intelligent computing techniques on medical image segmentation and analysis a...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
SINGLE IMAGE SUPER RESOLUTION: A COMPARATIVE STUDYcsandit
The majority of applications requiring high resolution images to derive and analyze data
accurately and easily. Image super resolution is playing an effective role in those applications.
Image super resolution is the process of producing high resolution image from low resolution
image. In this paper, we study various image super resolution techniques with respect to the
quality of results and processing time. This comparative study introduces a comparison between
four algorithms of single image super-resolution. For fair comparison, the compared algorithms
are tested on the same dataset and same platform to show the major advantages of one over the
others.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity
Detection of Diverse Tumefactions in Medial Images by Various Cumulation MethodsIRJET Journal
This document proposes and compares two methods - Particle Swarm Optimization (PSO) and Search Based Optimization - for detecting tumors in MRI and CT medical images. It first reviews previous work using techniques like PSO, cuckoo search, and evolutionary convolutional neural networks for tumor detection. It then describes the methodology, which involves preprocessing images, segmenting them using PSO and Search Based Optimization, classifying segments as tumor or non-tumor using Support Vector Machines, and extracting features to identify the tumor. Parameters like accuracy, processing time, and error are compared between the two optimization methods to determine which achieves a more accurate tumor shape detection.
Image Registration for Recovering Affine Transformation Using Nelder Mead Sim...CSCJournals
This paper proposes a parallel approach for the Vector Quantization (VQ) problem in image processing. VQ deals with codebook generation from the input training data set and replacement of any arbitrary data with the nearest codevector. Most of the efforts in VQ have been directed towards designing parallel search algorithms for the codebook, and little has hitherto been done in evolving a parallelized procedure to obtain an optimum codebook. This parallel algorithm addresses the problem of designing an optimum codebook using the traditional LBG type of vector quantization algorithm for shared memory systems and for the efficient usage of parallel processors. Using the codebook formed from a training set, any arbitrary input data is replaced with the nearest codevector from the codebook. The effectiveness of the proposed algorithm is indicated.
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.
Development and Comparison of Image Fusion Techniques for CT&MRI ImagesIJERA Editor
Image processing techniques primarily focus upon enhancing the quality of an image or a set ofimages to derive
the maximum information from them. Image Fusion is a technique of producing a superior quality image from a
set of available images. It is the process of combining relevant information from two or more images into a
single image wherein the resulting image will be more informative and complete than any of the input images. A
lot of research is being done in this field encompassing areas of Computer Vision, Automatic object detection,
Image processing, parallel and distributed processing, Robotics and remote sensing. This project paves way to
explain the theoretical and implementation issues of seven image fusion algorithms and the experimental results
of the same. The fusion algorithms would be assessed based on the study and development of some image
quality metrics
Feature Extraction of an Image by Using Adaptive Filtering and Morpological S...IOSR Journals
Abstract: For enhancing an image various enhancement schemes are used which includes gray scale manipulation, filtering and Histogram Equalization, Where Histogram equalization is one of the well known image enhancement technique. It became a popular technique for contrast enhancement because it is simple and effective. The basic idea of Histogram Equalization method is to remap the gray levels of an image. Here using morphological segmentation we can get the segmented image. Morphological reconstruction is used to segment the image. Comparative analysis of different enhancement and segmentation will be carried out. This comparison will be done on the basis of subjective and objective parameters. Subjective parameter is visual quality and objective parameters are Area, Perimeter, Min and Max intensity, Avg Voxel Intensity, Std Dev of Intensity, Eccentricity, Coefficient of skewness, Coefficient of Kurtosis, Median intensity, Mode intensity. Keywords: Histogram Equalization, Segmentation, Morphological Reconstruction .
Similar to Medical Image Segmentation by Transferring Ground Truth Segmentation Based upon Top Down and Bottom Up Approach (20)
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
A Comprehensive Guide to DeFi Development Services in 2024Intelisync
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
Dive into the realm of operating systems (OS) with Pravash Chandra Das, a seasoned Digital Forensic Analyst, as your guide. 🚀 This comprehensive presentation illuminates the core concepts, types, and evolution of OS, essential for understanding modern computing landscapes.
Beginning with the foundational definition, Das clarifies the pivotal role of OS as system software orchestrating hardware resources, software applications, and user interactions. Through succinct descriptions, he delineates the diverse types of OS, from single-user, single-task environments like early MS-DOS iterations, to multi-user, multi-tasking systems exemplified by modern Linux distributions.
Crucial components like the kernel and shell are dissected, highlighting their indispensable functions in resource management and user interface interaction. Das elucidates how the kernel acts as the central nervous system, orchestrating process scheduling, memory allocation, and device management. Meanwhile, the shell serves as the gateway for user commands, bridging the gap between human input and machine execution. 💻
The narrative then shifts to a captivating exploration of prominent desktop OSs, Windows, macOS, and Linux. Windows, with its globally ubiquitous presence and user-friendly interface, emerges as a cornerstone in personal computing history. macOS, lauded for its sleek design and seamless integration with Apple's ecosystem, stands as a beacon of stability and creativity. Linux, an open-source marvel, offers unparalleled flexibility and security, revolutionizing the computing landscape. 🖥️
Moving to the realm of mobile devices, Das unravels the dominance of Android and iOS. Android's open-source ethos fosters a vibrant ecosystem of customization and innovation, while iOS boasts a seamless user experience and robust security infrastructure. Meanwhile, discontinued platforms like Symbian and Palm OS evoke nostalgia for their pioneering roles in the smartphone revolution.
The journey concludes with a reflection on the ever-evolving landscape of OS, underscored by the emergence of real-time operating systems (RTOS) and the persistent quest for innovation and efficiency. As technology continues to shape our world, understanding the foundations and evolution of operating systems remains paramount. Join Pravash Chandra Das on this illuminating journey through the heart of computing. 🌟
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 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 🇨🇿 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 .
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
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performs the extraction of the hip joint manually. The boundaries of hip joint give the proper
understanding of abnormality of structure.
Several techniques have been developed for medical image segmentation. The results are not
accurate enough, so medical experts correct the results for medical surgery. In this paper, we
propose the novel method to transfer the ground truth segmentation from the database to the MR
image of another patient. The ground truth segmentation is manual segmentation done by the
medical experts. The algorithm is based on the top down approach to match the test image
globally and locally over the database images in order to find the best train image for each of the
local part of test image. The bottom up approach assembles all the best train parts that are
obtained from top down approach to represent the test image by the collection of train image
parts. The ground truth segmentation is transferred from train image parts to test image at the
respective location.
2. RELATED WORK
The different techniques have been developed for medical image segmentation over the years.
The medical image segmentation has been difficult task due to poor contrast, noise, intensity
variation and not clear understanding of boundaries of the parts in the image. The popular
techniques for image segmentation are based on the intensity of the pixels. The pixel with similar
gray scale values are considered as a region based upon the constraints. The region growing,
merging and splitting methods are region based image segmentation [4]. The region growing
method needs the initial seed point to start segmentation process. The method compares the initial
seed point with other neighbouring pixels to merge into one region. This is an iterative process [4]
[7]. The other techniques, the region merging and splitting are divided into two stages. First, the
region splitting involves the decomposition of the image into a number of regions based on some
criteria. Second part of the process involves the merging of decomposed parts. The merging of
regions is the searching and aggregation process into similar regions [7]. As both methods have
their advantages and disadvantages. As in [14], the region growing and region merging is
combined for the ultrasound medical image segmentation. The combination of different approach
like genetic algorithm, gradient based methods, wavelet processing, morphological methods with
region growing and merging used for better initial condition to start the segmentation of medical
image segmentation [13] [10].
The model based approach uses the prior knowledge for the segmentation of the object. The
approach makes benefit of prior model to get the approximation of the object to be expected in
the image. The top down strategy deform the model to fit with the data in the image. The model
deformation is done by different methods. The active contour techniques so called snake uses
energy minimization technique to deform the model [12]. The method is based on two energy
function, internal and external energy. The internal energy keeps the closer to the prior model and
gives smoothness to the curve. The external energy moves model towards salient features of
image. The total energy is the summation of internal and external energy. The minimization of
total energy results the segmentation of image. There is different version of snakes like gradient
field snake [16], level set approach based on Mumford shah model [17]. The statistical model
based on training set of shape that we want detect in the image. The training set consists of
images of changing shapes that we want to detect in the image. The deformation of the model is
obtained by the statistical properties of large number of shapes in the training set. The methods
use only the shape constraint is called active shape model based segmentation [8] [9] [11] and the
method uses the shape and image information like texture, salient feature is called active
appearance model. The small training set of shapes causes the segmentation problems like holes
in the final segment, over segmentation and many more.
3. Computer Science & Information Technology (CS & IT) 119
In the field of medical image analysis, the object recognition and boundary detection of the organ
in a medical image is very important to delineate their shape. For a proper segmentation, it is
important to segment the local and global parts of the object. The top down approach gives a
global detection and bottom up approach start from a low level and the results the object shape.
Both the approach has their own importance, without the global detection, it is difficult to get
proper local structural information. The prior model is used for searching the object in the image.
The model is transformed to find similarity with objects in the image [3] [15]. After the global
detection, the local structure matching is important to get acceptable segmentation. As in [6], the
local part based template matching is used for human detection and segmentation.
The medical image segmentation is obtained from existing algorithms still corrected by the expert
people. We develop the novel method to transfer the expert people segmentation by the shape
matching algorithm based on the hausdorff distance to extract the boundaries of hip joint structure
in the MR image of the pelvic region.
3. METHODOLOGY
Figure 1: Workflow of the system
The method is the top down approach for shape matching based on the hausdorff distance of test
image with train images and bottom up strategy to represent the test image from the collection of
best train image parts. So, the ground truth segmentation can be transferred to test image.
3.1. Hausdorff distance
The hausdorff distance is the metric to measure the degree of mismatch between two shapes of
images. The hausdorff distance is max-min distance between the two sets of points. To calculate
the distance between two images, the boundaries are extracted from both test and train image. The
edge image represents the set of points for test and train image. Given a test image A and train
image B, the hausdorff distance is defined as
Where
The h (A,B) is the directed Hausdorff distance. It is defined as by considering every point of A,
calculating the distance from that point to the closest point of B and evaluate the maximum
4. 120 Computer Science & Information Technology (CS & IT)
among them [3]. The hausdorff distance is sensitive to the noise and outliers. The hausdorff
distance is modified to fix this problem. The parts of shapes are compared. The directed partial
hausdorff distance is defined as
,
Where denotes the Kth ranked value among the measured distances. The every point of A,
calculate the distance from that point to the closest point of B and then points of B are sorted
according to their distances and the Kth value will indicate the K of the model point of A is within
the distance of d with some points of A.
The partial hausdorff distance gives bad results with corrupted data; we need more a robust
measure to solve the problem with corrupted data. The least trimmed square (LTS-HD) hausdorff
distance is robust measure. It is defined by the linear combination of order statistics. LTS
hausdorff distance is defined as
Where k is K=f.N, the N is the number of points in the chunk of A. We used LTS-HD as a
similarity metric to detect test image model in the database of train images [5].
3.2. Simulated annealing optimization
The simulated annealing is the search algorithm which finds the optimal solution in the search
space. It is based on the probabilistic method to find the global optimum solution of the function
in the given search space. The algorithm is influenced by the annealing process of the metal in the
thermodynamics. The annealing process heats up the metal at high temperature to excite the
molecules of the metal. At high temperature, it is possible to change the structure of the metal.
The metal undergoes through the cooling process to obtain new physical structure. The
temperature is reduced gradually to obtain the desired structure of the metal. The temperature is
kept as a variable to simulate the heating and cooling process. The initial temperature and random
solution are important parameters to start the algorithm [3]. When the algorithm is at high
temperature, it will accept the more solution. This step will avoid the local optimum solution as it
comes along the path of finding a global optimum solution. The temperature of the system is
reduced gradually to work on the limited solution. The system can accept a worse solution, so
algorithm concentrate on the search area where we can find the global optimum solution. The
major advantage of the simulated annealing is not stuck in the local optima but search for the
global optimum solution.
In our system, we have used the simulated annealing optimization to minimize the LTS hausdorff
distance to detect the test image model in the train image dataset. The initial and final temperature
is very important parameter to obtain desired solution.
3.3. Database of MR images
The segmentation of MR images is divided into three categories.1) Automatic 2) semi automatic
3) manual segmentation. The automatic segmentation is based on the intensity of the pixels in the
image. The semi automatic segmentation needs human intervention to select the region of interest
for segmentation. The human can recognize the boundaries and shape of the object of interest
5. Computer Science & Information Technology (CS & IT) 121
more accurately than the computer algorithm [1]. The manual segmentation is most accurate
segmentation among all of them. We created a knowledge base of ground truth segmentation of
MR images of hip joint structure. The MR images are segmented manually by the medical
experts. The MR images are collected with their ground truth segmentation and then stored in the
database. We used the database images knowledge to segment the MR image of other patients.
The database images are named as train images. The knowledge 1base consists of 20 images.
4. TOP DOWN AND BOTTOM UP APPROACH
4.1. Global detection of test image
The main objective of top down approach is to detect global and local structure of the test image
in the train images. In the hausdorff distance based shape matching is used for detection of the
test image in a train image. The canny edge detection algorithm is used for the extraction of
boundaries of both test and train images. Given a test image M and train image I1 , I2 .....In, the
objective of shape matching is to obtain a transformation T to find similarity by the minimization
of hausdorff distance as the similarity metric. The affine transformation maps the point in one
plane to the other. The affine transformation parameter is scaling, rotation and translation. Let p=
(x, y) represents the point of test image, then the transformation is defined as
Where Sx and Sy are the scaling parameters in x and y direction. The translation parameters are tx
and ty in x and y direction and the rotation parameter either in a clockwise or anticlockwise
direction. The transformed point (x2, y2) is close to the train image points. The simulated annealing
search for best possible transformation in the given search space to minimize hausdorff distance
to make test image more similar to the train image. The test image is registered with all the train
images in the database. Then each of the registration gives the hausdorff distance and affine
transformation parameters. The train images are sorted in the ascending order according to the
hausdorff distance, half of the train images are selected from database for further processing. So,
out of the 20 images only 10 images are selected.
4.2. Hierarchical tree based local part registration
As in the previous section, the train images are selected with their respective transformation
parameters. The scaling and rotation is used for the transformation of the test image. Thus, the
total number of transformed images is ten. Each of the transformed test images is decomposed
into four parts as shown in figure 2. The hierarchical tree is constructed by placing decomposed
parts into tree as shown in figure 2. The tree has three levels denoted by Li, i= 0, 1, 2. Each level
has transformed test image and decomposed for the next level like level 0 (L0) which has only
transformed test image. At each level, the local parts are registered with the selected train images.
The train image is again sorted in ascending order according to the hausdorff distance obtain in
registration process of local parts. The transformed test parts at the level 1 (L1) are denoted as
Part 1, Part 2, Part 3, Part 4. Each of the tree nodes consists of 10 local parts. For example, Part_1
has 10 images and each of the Part_1 is registered with selected train image. The train images are
sorted according to the hausdorff distance and first half of the train images are selected for their
respective test image parts. For Part_1, the I10, I13, I14, I15, I16 is selected for next level registration.
The transformation obtained in L1 is used for next level registration.
6. 122 Computer Science & Information Technology (CS & IT)
The level 2 has 80 local parts. Each of the parts of level 1 is transformed and decomposed into
four parts for the next level. The level 2 parts are represented as Part j_k. The j represents parts of
level 1 and k represent the decomposition of level 1 part into level 2.So, Part 1_2 means Part1 of
level1 is decomposed into four parts and it is the second decomposition of Part 1 of level 1.At
level 2 (L2) , the decomposed parts are registered with selected train images. For L2, rotation and
translation is used as transformation parameter for local part registration based on hausdorff
distance. The train images for L2 are sorted in ascending order according their respective
hausdorff distance. The train image with lowest hausdorff distance is selected with their
transformation as a best train image for its corresponding test image local part at level 2. Finally,
each of the local part of level 2 are transformed and matched with their best train image. So, there
are 16 local parts with their 16 best train images. We stop the decomposition of test image parts at
level 2.
Figure 2: Top down approach of shape matching
4.3. Bottom up approach
The bottom up approach aggregates all best train image parts, and represent the test image from
the collection of best train image parts. As in the previous section, each of the local part has their
corresponding best train image. For each of the local part of test images, train image part is
cropped to the same size of test image local parts at L2. The local test image parts of L2 are
replaced by the corresponding train image part. The transformation obtained after the level 2 local
part registrations is inversed and applied to the best train image part. The level 2 consists of best
train image parts which are transformed inversely. The inverse scaling parameters are 1/Sx and
1/Sy in x and y direction. The inverse rotation is negative of angle of rotation; if the top down
parameter is clock wise then inverse rotation is anti-clockwise. The –tx and ty is the inverse
translation in x and y direction. As we climb up the tree, at every level, we merge the best train
parts into one region and as we can see in the figure 3, the final image is the mosaic of best train
image parts.
The test image is symbolized by the collection of best train image parts. The database consists of
ground truth of these train images. The ground truth segmentation from the train image parts of
the test image to their corresponding location. Finally, the segmentation of test image is the
collection of the ground truth segmentation of train images.
7. Computer Science & Information Technology (CS & IT) 123
Figure 3: Bottom a test image to represent test image by the train image parts
5. EXPERIMENTAL RESULTS
The database consists of 20 MR images of size 256 x 256 of hip joint structure with their ground
truth segmentation. The size of test image is 155 x 123. The canny edge detector is used for
extraction of boundaries of test and train images. The threshold and sigma are 0.545 and 4 for the
canny edge detector. The simulated annealing initial temperature parameter is 100. The affine
transformation parameter for global detection is 0.863≤Sx≤0.982 and 0.79≤Sy≤0.97 as a scaling
parameter in x and y direction. The rotation parameter is from -10 to 10 and translation parameter
is 0≤tx≤400 and 0≤ty≤400 in x and y direction.
(a) (b) (c)
Figure 4: (a) Test image (b) MR image database and (c) Transformed test image.
8. 124 Computer Science & Information Technology (CS & IT)
Figure 5: The global registration of test image over train image.
(a) (b)
Figure 6: (a) Decomposed test parts at level one and (b) Local part registration.
(a) (b)
Figure 7: (a) shows local parts at level two and (b) shows its registration with train images.
9. Computer Science & Information Technology (CS & IT) 125
Figure 8: The segmentation of test image from ground truth data
6. CONCLUSIONS
We have proposed the novel method of image segmentation of hip joint structure in MR image.
The method is to transfer ground truth segmentation from the train image database to the test
image. The train image database consists of MR images of hip joint structure with their ground
truth segmentation. The ground truth segmentation is done by medical experts. The method based
on the top down approach to register test image globally and locally with the train images. The
top down approach uses the hausdorff distance based shape registration algorithm. The objective
of the top down approach is to find the best train image for each of the local test image parts. The
bottom up approach uses the inverse transformation to match best train image parts with the
original test image and the test image is represented by the mosaic of best train image parts. The
ground truth segmentation from train image parts to their respective location of test image.
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