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Magnetic resonance image segmentation for knee osteoarthritis using active shape models

Knee osteoarthritis is a chronic joint inflammation disease that affects the aged population nowadays. The disease leads to gradual degradation of cartilage and thus deteriorates the function of the knee joint. Magnetic Resonance Imaging (MRI) provides promising results for the early detection of kn...

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Main Authors:	Soh, S. S., Tan, Tian Swee, Sim, Siew Ying, Chuah, Zhi En, Mazenan, M. N., Leong, Kah Meng
Format:	Conference or Workshop Item
Published:	2015
Subjects:	TP Chemical technology
Online Access:	http://eprints.utm.my/id/eprint/59361/ http://dx.doi.org/10.1109/BMEiCON.2014.7017365
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id	my.utm.59361
record_format	eprints
spelling	my.utm.593612021-08-17T09:08:44Z http://eprints.utm.my/id/eprint/59361/ Magnetic resonance image segmentation for knee osteoarthritis using active shape models Soh, S. S. Tan, Tian Swee Sim, Siew Ying Chuah, Zhi En Mazenan, M. N. Leong, Kah Meng TP Chemical technology Knee osteoarthritis is a chronic joint inflammation disease that affects the aged population nowadays. The disease leads to gradual degradation of cartilage and thus deteriorates the function of the knee joint. Magnetic Resonance Imaging (MRI) provides promising results for the early detection of knee osteoarthritis. Conventionally, the MR image segmentation for knee osteoarthritis is manually done by clinicians. Limitations of this process include being laborious, time-consuming and prone to subjective diagnosis error. Therefore, the development of an automated cartilage segmentation method is crucial to assist the medical research in knee osteoarthritis. This project applied the Active Shape Models (ASM) approach to create semi-automated cartilage segmentation software. A shape model was constructed from a training set consisting of 10 knee MR images which includes major variations of the knee cartilage shape. Principle component analysis (PCA) was utilized to identify the main axes of variations used to build the shape model. This shape model was finally used to segment the knee articular cartilage. Outcomes of the ASM segmentation were compared with the outcome of manual segmentation. Experimental results showed that the sensitivity of developed ASM approach increased averagely from 73.78% to 80.75%, proportional to the increasing of the number of iteration in the segmentation as well as landmark of the shape model. This technique is reliable to contribute to medical research in knee osteoarthritis by providing an efficient and high accuracy segmentation method for knee articular cartilage, to further assist in the detection of knee osteoarthritis via MRI technique. 2015-01-20 Conference or Workshop Item PeerReviewed Soh, S. S. and Tan, Tian Swee and Sim, Siew Ying and Chuah, Zhi En and Mazenan, M. N. and Leong, Kah Meng (2015) Magnetic resonance image segmentation for knee osteoarthritis using active shape models. In: 7th Biomedical Engineering International Conference, BMEiCON 2014, 26 November 2014 - 28 November 2014, Fukuoka, Japan. http://dx.doi.org/10.1109/BMEiCON.2014.7017365
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	TP Chemical technology
spellingShingle	TP Chemical technology Soh, S. S. Tan, Tian Swee Sim, Siew Ying Chuah, Zhi En Mazenan, M. N. Leong, Kah Meng Magnetic resonance image segmentation for knee osteoarthritis using active shape models
description	Knee osteoarthritis is a chronic joint inflammation disease that affects the aged population nowadays. The disease leads to gradual degradation of cartilage and thus deteriorates the function of the knee joint. Magnetic Resonance Imaging (MRI) provides promising results for the early detection of knee osteoarthritis. Conventionally, the MR image segmentation for knee osteoarthritis is manually done by clinicians. Limitations of this process include being laborious, time-consuming and prone to subjective diagnosis error. Therefore, the development of an automated cartilage segmentation method is crucial to assist the medical research in knee osteoarthritis. This project applied the Active Shape Models (ASM) approach to create semi-automated cartilage segmentation software. A shape model was constructed from a training set consisting of 10 knee MR images which includes major variations of the knee cartilage shape. Principle component analysis (PCA) was utilized to identify the main axes of variations used to build the shape model. This shape model was finally used to segment the knee articular cartilage. Outcomes of the ASM segmentation were compared with the outcome of manual segmentation. Experimental results showed that the sensitivity of developed ASM approach increased averagely from 73.78% to 80.75%, proportional to the increasing of the number of iteration in the segmentation as well as landmark of the shape model. This technique is reliable to contribute to medical research in knee osteoarthritis by providing an efficient and high accuracy segmentation method for knee articular cartilage, to further assist in the detection of knee osteoarthritis via MRI technique.
format	Conference or Workshop Item
author	Soh, S. S. Tan, Tian Swee Sim, Siew Ying Chuah, Zhi En Mazenan, M. N. Leong, Kah Meng
author_facet	Soh, S. S. Tan, Tian Swee Sim, Siew Ying Chuah, Zhi En Mazenan, M. N. Leong, Kah Meng
author_sort	Soh, S. S.
title	Magnetic resonance image segmentation for knee osteoarthritis using active shape models
title_short	Magnetic resonance image segmentation for knee osteoarthritis using active shape models
title_full	Magnetic resonance image segmentation for knee osteoarthritis using active shape models
title_fullStr	Magnetic resonance image segmentation for knee osteoarthritis using active shape models
title_full_unstemmed	Magnetic resonance image segmentation for knee osteoarthritis using active shape models
title_sort	magnetic resonance image segmentation for knee osteoarthritis using active shape models
publishDate	2015
url	http://eprints.utm.my/id/eprint/59361/ http://dx.doi.org/10.1109/BMEiCON.2014.7017365
_version_	1709667353443172352
score	13.211869

Magnetic resonance image segmentation for knee osteoarthritis using active shape models

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