Medical image analysis of knee images for sarcopenia detection using mri

Osteoarthritis is a degenerative syndrome with health impairment leads to decrease in their physical activities, inflammation, stiffness and severe pain in the elderly population. It is necessary for us to know about the mechanism behind Progressive loss of cartilage for the efficient diagnosis a...

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Main Author: Chitradevi
Other Authors: Poh Chueh Loo
Format: Theses and Dissertations
Language:English
Published: 2014
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Online Access:http://hdl.handle.net/10356/61099
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-610992023-03-03T15:57:25Z Medical image analysis of knee images for sarcopenia detection using mri Chitradevi Poh Chueh Loo School of Chemical and Biomedical Engineering DRNTU::Engineering Osteoarthritis is a degenerative syndrome with health impairment leads to decrease in their physical activities, inflammation, stiffness and severe pain in the elderly population. It is necessary for us to know about the mechanism behind Progressive loss of cartilage for the efficient diagnosis and treatment of different stages of osteoarthritis. Now the fast world is expecting an easy and non-invasive technique for diagnosing the disease faster with at most accuracy. So we are going for MRI, which is the most prominent widely used tool to access intra articular surfaces, cartilage in particular. The purpose of calculating the thickness of cartilage is to understand the progressive loss of cartilage and to study the clinical interventions. The aim of the project is to extract and analyze the femoral articular cartilage of the knee joint through medical image analysis. An MRI image was segmented using a number of software, including Matlab and volumetric analysis was performed using the results obtained from these images. We performed manual segmentation, providing anatomical information; in detecting the physical changes in their cartilage structure. Through this we facilitate the clinician to visualize the large amount of data of a particular patient within limited time. Semi–Automatic segmentation include thresholding which would be partially solved by deformable methods, while extracting the cartilage tissue there will be biggest variation in every OA subjects with cartilage in homogeneities and with minimum inter-tissue contrast. Our result helps to analyze the anatomical knee images of five patients and to visualize the thickness of cartilage more rapidly. ​Master of Science (Biomedical Engineering) 2014-06-04T08:48:26Z 2014-06-04T08:48:26Z 2014 2014 Thesis http://hdl.handle.net/10356/61099 en 54 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering
spellingShingle DRNTU::Engineering
Chitradevi
Medical image analysis of knee images for sarcopenia detection using mri
description Osteoarthritis is a degenerative syndrome with health impairment leads to decrease in their physical activities, inflammation, stiffness and severe pain in the elderly population. It is necessary for us to know about the mechanism behind Progressive loss of cartilage for the efficient diagnosis and treatment of different stages of osteoarthritis. Now the fast world is expecting an easy and non-invasive technique for diagnosing the disease faster with at most accuracy. So we are going for MRI, which is the most prominent widely used tool to access intra articular surfaces, cartilage in particular. The purpose of calculating the thickness of cartilage is to understand the progressive loss of cartilage and to study the clinical interventions. The aim of the project is to extract and analyze the femoral articular cartilage of the knee joint through medical image analysis. An MRI image was segmented using a number of software, including Matlab and volumetric analysis was performed using the results obtained from these images. We performed manual segmentation, providing anatomical information; in detecting the physical changes in their cartilage structure. Through this we facilitate the clinician to visualize the large amount of data of a particular patient within limited time. Semi–Automatic segmentation include thresholding which would be partially solved by deformable methods, while extracting the cartilage tissue there will be biggest variation in every OA subjects with cartilage in homogeneities and with minimum inter-tissue contrast. Our result helps to analyze the anatomical knee images of five patients and to visualize the thickness of cartilage more rapidly.
author2 Poh Chueh Loo
author_facet Poh Chueh Loo
Chitradevi
format Theses and Dissertations
author Chitradevi
author_sort Chitradevi
title Medical image analysis of knee images for sarcopenia detection using mri
title_short Medical image analysis of knee images for sarcopenia detection using mri
title_full Medical image analysis of knee images for sarcopenia detection using mri
title_fullStr Medical image analysis of knee images for sarcopenia detection using mri
title_full_unstemmed Medical image analysis of knee images for sarcopenia detection using mri
title_sort medical image analysis of knee images for sarcopenia detection using mri
publishDate 2014
url http://hdl.handle.net/10356/61099
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