Knee cartilage segmentation using multi purpose interactive approach

Interactive model incorporates expert interpretation and automated segmentation. However, cartilage has complicated structure, indistinctive tissue contrast in magnetic resonance image of knee hardens image review and existing interactive methods are sensitive to various technical problems such as b...

全面介绍

Saved in:

书目详细资料
主要作者:	Gan, Hong Seng
格式:	Thesis
语言:	English
出版:	2016
主题:	QH Natural history
在线阅读:	http://eprints.utm.my/id/eprint/78034/1/GanHongSengPFBME2016.pdf
标签:	添加标签没有标签, 成为第一个标记此记录!

id	my-utm-ep.78034
record_format	uketd_dc
spelling	my-utm-ep.780342018-07-23T06:05:27Z Knee cartilage segmentation using multi purpose interactive approach 2016-01 Gan, Hong Seng QH Natural history Interactive model incorporates expert interpretation and automated segmentation. However, cartilage has complicated structure, indistinctive tissue contrast in magnetic resonance image of knee hardens image review and existing interactive methods are sensitive to various technical problems such as bi-label segmentation problem, shortcut problem and sensitive to image noise. Moreover, redundancy issue caused by non-cartilage labelling has never been tackled. Therefore, Bi-Bezier Curve Contrast Enhancement is developed to improve visual quality of magnetic resonance image by considering brightness preservation and contrast enhancement control. Then, Multipurpose Interactive Tool is developed to handle users’ interaction through Label Insertion Point approach. Approximate NonCartilage Labelling system is developed to generate computerized non-cartilage label, while preserves cartilage for expert labelling. Both computerized and interactive labels initialize Random Walks based segmentation model. To evaluate contrast enhancement techniques, Measure of Enhancement (EME), Absolute Mean Brightness Error (AMBE) and Feature Similarity Index (FSIM) are used. The results suggest that Bi-Bezier Curve Contrast Enhancement outperforms existing methods in terms of contrast enhancement control (EME = 41.44±1.06), brightness distortion (AMBE = 14.02±1.29) and image quality (FSIM = 0.92±0.02). Besides, implementation of Approximate Non-Cartilage Labelling model has demonstrated significant efficiency improvement in segmenting normal cartilage (61s±8s, P = 3.52 x 10-5) and diseased cartilage (56s±16s, P = 1.4 x 10-4). Finally, the proposed labelling model has high Dice values (Normal: 0.94±0.022, P = 1.03 x 10-9; Abnormal: 0.92±0.051, P = 4.94 x 10-6) and is found to be beneficial to interactive model (+0.12). 2016-01 Thesis http://eprints.utm.my/id/eprint/78034/ http://eprints.utm.my/id/eprint/78034/1/GanHongSengPFBME2016.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:97286 phd doctoral Universiti Teknologi Malaysia, Faculty of Biosciences and Medical Engineering Faculty of Biosciences and Medical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QH Natural history
spellingShingle	QH Natural history Gan, Hong Seng Knee cartilage segmentation using multi purpose interactive approach
description	Interactive model incorporates expert interpretation and automated segmentation. However, cartilage has complicated structure, indistinctive tissue contrast in magnetic resonance image of knee hardens image review and existing interactive methods are sensitive to various technical problems such as bi-label segmentation problem, shortcut problem and sensitive to image noise. Moreover, redundancy issue caused by non-cartilage labelling has never been tackled. Therefore, Bi-Bezier Curve Contrast Enhancement is developed to improve visual quality of magnetic resonance image by considering brightness preservation and contrast enhancement control. Then, Multipurpose Interactive Tool is developed to handle users’ interaction through Label Insertion Point approach. Approximate NonCartilage Labelling system is developed to generate computerized non-cartilage label, while preserves cartilage for expert labelling. Both computerized and interactive labels initialize Random Walks based segmentation model. To evaluate contrast enhancement techniques, Measure of Enhancement (EME), Absolute Mean Brightness Error (AMBE) and Feature Similarity Index (FSIM) are used. The results suggest that Bi-Bezier Curve Contrast Enhancement outperforms existing methods in terms of contrast enhancement control (EME = 41.44±1.06), brightness distortion (AMBE = 14.02±1.29) and image quality (FSIM = 0.92±0.02). Besides, implementation of Approximate Non-Cartilage Labelling model has demonstrated significant efficiency improvement in segmenting normal cartilage (61s±8s, P = 3.52 x 10-5) and diseased cartilage (56s±16s, P = 1.4 x 10-4). Finally, the proposed labelling model has high Dice values (Normal: 0.94±0.022, P = 1.03 x 10-9; Abnormal: 0.92±0.051, P = 4.94 x 10-6) and is found to be beneficial to interactive model (+0.12).
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Gan, Hong Seng
author_facet	Gan, Hong Seng
author_sort	Gan, Hong Seng
title	Knee cartilage segmentation using multi purpose interactive approach
title_short	Knee cartilage segmentation using multi purpose interactive approach
title_full	Knee cartilage segmentation using multi purpose interactive approach
title_fullStr	Knee cartilage segmentation using multi purpose interactive approach
title_full_unstemmed	Knee cartilage segmentation using multi purpose interactive approach
title_sort	knee cartilage segmentation using multi purpose interactive approach
granting_institution	Universiti Teknologi Malaysia, Faculty of Biosciences and Medical Engineering
granting_department	Faculty of Biosciences and Medical Engineering
publishDate	2016
url	http://eprints.utm.my/id/eprint/78034/1/GanHongSengPFBME2016.pdf
_version_	1747817891086991360

Knee cartilage segmentation using multi purpose interactive approach

相似书籍