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Colour image segmentation of tuberculosis bacilli in Ziehl-Neelsen-stained tissue images using moving K-Mean clustering procedure

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Bibliographic Details
Main Authors: M. K. Osman, Mohd Yusoff, Mashor, Prof Madya Dr., Z. Saad, H. Jaafar
Other Authors: khusairi@ppinang.uitm.edu.my
Format: Working Paper
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE) 2010
Subjects:
Colour image segmentation
Tuberculosis bacilli
Tissue section
Moving k-mean clustering
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/10174
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spelling my.unimap-101742010-11-10T07:50:18Z Colour image segmentation of tuberculosis bacilli in Ziehl-Neelsen-stained tissue images using moving K-Mean clustering procedure M. K. Osman Mohd Yusoff, Mashor, Prof Madya Dr. Z. Saad H. Jaafar khusairi@ppinang.uitm.edu.my Colour image segmentation Tuberculosis bacilli Tissue section Moving k-mean clustering Link to publisher's homepage at http://ieeexplore.ieee.org/ Segmentation of tuberculosis bacilli in Zeihl-Neelsen tissue slide images is a crucial step in computerassisted tuberculosis bacilli detection. In this paper, an automatic colour image segmentation using moving k-mean clustering was proposed. First, initial filter is used to remove the tissues images which remain blue after counterstaining process. After that, moving k-mean clustering using green component of RGB colour model and Rycomponent of C-Y colour model are used to segment the TB bacilli from its undesirable background which also remains red even after decolourization process. Then 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. Experimental results indicate that the proposed techniques were successfully segment TB bacilli from its background. 2010-11-09T09:36:44Z 2010-11-09T09:36:44Z 2010-05 Working Paper p.215-220 978-1-4244-7196-6 http://ezproxy.unimap.edu.my:2080/stamp/stamp.jsp?tp=&arnumber=5489226 http://hdl.handle.net/123456789/10174 en Proceedings of the 4th Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation 2010 Institute of Electrical and Electronics Engineers (IEEE)
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 Colour image segmentation
Tuberculosis bacilli
Tissue section
Moving k-mean clustering
spellingShingle Colour image segmentation
Tuberculosis bacilli
Tissue section
Moving k-mean clustering
M. K. Osman
Mohd Yusoff, Mashor, Prof Madya Dr.
Z. Saad
H. Jaafar
Colour image segmentation of tuberculosis bacilli in Ziehl-Neelsen-stained tissue images using moving K-Mean clustering procedure
description Link to publisher's homepage at http://ieeexplore.ieee.org/
author2 khusairi@ppinang.uitm.edu.my
author_facet khusairi@ppinang.uitm.edu.my
M. K. Osman
Mohd Yusoff, Mashor, Prof Madya Dr.
Z. Saad
H. Jaafar
format Working Paper
author M. K. Osman
Mohd Yusoff, Mashor, Prof Madya Dr.
Z. Saad
H. Jaafar
author_sort M. K. Osman
title Colour image segmentation of tuberculosis bacilli in Ziehl-Neelsen-stained tissue images using moving K-Mean clustering procedure
title_short Colour image segmentation of tuberculosis bacilli in Ziehl-Neelsen-stained tissue images using moving K-Mean clustering procedure
title_full Colour image segmentation of tuberculosis bacilli in Ziehl-Neelsen-stained tissue images using moving K-Mean clustering procedure
title_fullStr Colour image segmentation of tuberculosis bacilli in Ziehl-Neelsen-stained tissue images using moving K-Mean clustering procedure
title_full_unstemmed Colour image segmentation of tuberculosis bacilli in Ziehl-Neelsen-stained tissue images using moving K-Mean clustering procedure
title_sort colour image segmentation of tuberculosis bacilli in ziehl-neelsen-stained tissue images using moving k-mean clustering procedure
publisher Institute of Electrical and Electronics Engineers (IEEE)
publishDate 2010
url http://dspace.unimap.edu.my/xmlui/handle/123456789/10174
_version_ 1643789760839286784
score 13.160551

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