Staff View: Application of fuzzy c-mean clustering technique for mycobacterium tuberculosis detection in Ziehl-Neelsen stained tissue images

Application of fuzzy c-mean clustering technique for mycobacterium tuberculosis detection in Ziehl-Neelsen stained tissue images

International Postgraduate Conference On Engineering (IPCE 2010), 16th - 17th October 2010 organized by Centre for Graduate Studies, Universiti Malaysia Perlis (UniMAP) at School of Mechatronic Engineering, Pauh Putra Campus, Perlis, Malaysia.

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Bibliographic Details
Main Authors:	Muhammad Khusairi, Osman, Mohd Yusoff, Mashor, Prof. Dr.
Other Authors:	khusairi@ppinang.uitm.edu.my
Format:	Working Paper
Language:	English
Published:	Universiti Malaysia Perlis (UniMAP) 2012
Subjects:	Mycobacterium tuberculosis Zeihl-Neelsen stained tissue images Tuberculosis (TB)
Online Access:	http://dspace.unimap.edu.my/xmlui/handle/123456789/21497
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id	my.unimap-21497
record_format	dspace
spelling	my.unimap-214972012-10-21T08:32:00Z Application of fuzzy c-mean clustering technique for mycobacterium tuberculosis detection in Ziehl-Neelsen stained tissue images Muhammad Khusairi, Osman Mohd Yusoff, Mashor, Prof. Dr. khusairi@ppinang.uitm.edu.my Mycobacterium tuberculosis Zeihl-Neelsen stained tissue images Tuberculosis (TB) International Postgraduate Conference On Engineering (IPCE 2010), 16th - 17th October 2010 organized by Centre for Graduate Studies, Universiti Malaysia Perlis (UniMAP) at School of Mechatronic Engineering, Pauh Putra Campus, Perlis, Malaysia. Automatic detection of Mycobacterium tuberculosis improves accuracy, sensitivity and efficiency of diagnosis compared to manual method. However, the process is difficult, especially in Zeihl-Neelsen stained tissue images due to intensity inhomogeneity and tissue background complexity. In this paper, an automated approach to segment Mycobacterium tuberculosis from tissue slide images using fuzzy c-mean clustering procedure is proposed. The procedure provides a basic step for detecting the presence of tuberculosis bacilli. First, initial filter is used to assist the clustering process by removing the tissues images which remain blue after counterstaining process. Then, fuzzy c-mean clustering is applied to segment the bacilli. Three colour models, RGB, HSI and C-Y are analysed to identify the colour model that perform significant segmentation performance. Finally, a 5×5 median filter and region growing was used to eliminate small regions and noises. The proposed methods have been analysed for several TB slide images under various conditions. The results indicated that fuzzy c-mean clustering using saturation component of C-Y colour model has achieved the best segmentation result with an accuracy of 99.54%. 2012-10-21T08:31:59Z 2012-10-21T08:31:59Z 2010-10-16 Working Paper 978-967-5760-03-7 http://hdl.handle.net/123456789/21497 en Proceedings of the International Postgraduate Conference on Engineering (IPCE 2010) Universiti Malaysia Perlis (UniMAP) Centre for Graduate Studies
institution	Universiti Malaysia Perlis
building	UniMAP Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Perlis
content_source	UniMAP Library Digital Repository
url_provider	http://dspace.unimap.edu.my/
language	English
topic	Mycobacterium tuberculosis Zeihl-Neelsen stained tissue images Tuberculosis (TB)
spellingShingle	Mycobacterium tuberculosis Zeihl-Neelsen stained tissue images Tuberculosis (TB) Muhammad Khusairi, Osman Mohd Yusoff, Mashor, Prof. Dr. Application of fuzzy c-mean clustering technique for mycobacterium tuberculosis detection in Ziehl-Neelsen stained tissue images
description	International Postgraduate Conference On Engineering (IPCE 2010), 16th - 17th October 2010 organized by Centre for Graduate Studies, Universiti Malaysia Perlis (UniMAP) at School of Mechatronic Engineering, Pauh Putra Campus, Perlis, Malaysia.
author2	khusairi@ppinang.uitm.edu.my
author_facet	khusairi@ppinang.uitm.edu.my Muhammad Khusairi, Osman Mohd Yusoff, Mashor, Prof. Dr.
format	Working Paper
author	Muhammad Khusairi, Osman Mohd Yusoff, Mashor, Prof. Dr.
author_sort	Muhammad Khusairi, Osman
title	Application of fuzzy c-mean clustering technique for mycobacterium tuberculosis detection in Ziehl-Neelsen stained tissue images
title_short	Application of fuzzy c-mean clustering technique for mycobacterium tuberculosis detection in Ziehl-Neelsen stained tissue images
title_full	Application of fuzzy c-mean clustering technique for mycobacterium tuberculosis detection in Ziehl-Neelsen stained tissue images
title_fullStr	Application of fuzzy c-mean clustering technique for mycobacterium tuberculosis detection in Ziehl-Neelsen stained tissue images
title_full_unstemmed	Application of fuzzy c-mean clustering technique for mycobacterium tuberculosis detection in Ziehl-Neelsen stained tissue images
title_sort	application of fuzzy c-mean clustering technique for mycobacterium tuberculosis detection in ziehl-neelsen stained tissue images
publisher	Universiti Malaysia Perlis (UniMAP)
publishDate	2012
url	http://dspace.unimap.edu.my/xmlui/handle/123456789/21497
_version_	1643793408697827328
score	13.211869

Application of fuzzy c-mean clustering technique for mycobacterium tuberculosis detection in Ziehl-Neelsen stained tissue images

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