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Unsupervised colour segmentation of white blood cell for acute leukaemia images

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Main Authors: A. S., Abdul Nasir, Mohd Yusoff, Mashor, Prof. Dr., H., Rosline
Other Authors: aimi_salihah@yahoo.com
Format: Working Paper
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE) 2011
Subjects:
Acute leukemia
Colour segmentation
HSI colour space
K-means clustering
Linear contrast
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/14075
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spelling my.unimap-140752014-06-27T09:48:20Z Unsupervised colour segmentation of white blood cell for acute leukaemia images A. S., Abdul Nasir Mohd Yusoff, Mashor, Prof. Dr. H., Rosline aimi_salihah@yahoo.com roslin@kb.usm.my Acute leukemia Colour segmentation HSI colour space K-means clustering Linear contrast Link to publisher's homepage at http://ieeexplore.ieee.org/ Colour image segmentation has becoming more popular for computer vision due to its important process in most medical analysis tasks. One of the main tasks is the segmentation of white blood cell (WBC) where the WBC composition reveals important diagnostic information of a patient. In this paper, the combination between linear contrast technique and colour segmentation based on HSI (Hue, Saturation, Intensity) colour space were used in order to obtain a fully segmented abnormal WBC and nucleus of acute leukaemia images. The unsupervised segmentation technique namely k-means clustering algorithm is used to ease the segmentation process. By implementing the proposed segmentation technique, the fully segmented WBC which consists of cytoplasm and nucleus regions can be achieved by using the combination of linear contrast technique and segmentation based on H component image. Meanwhile, the fully segmented nucleus can be obtained by applying the segmentation based on S component image. The combinations between linear contrast technique and segmentation based on HSI colour space have produced a better effect on improving the accuracy of WBC segmentation with segmentation accuracies of 99.02% and 99.05% for segmented WBC and nucleus, respectively. 2011-10-08T07:42:14Z 2011-10-08T07:42:14Z 2011 Working Paper p. 142-145 978-1-6128-4896-9 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5962188&tag=1 http://hdl.handle.net/123456789/14075 en Proceedings of the International Conference on Imaging Systems and Techniques (IST 2011) 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 Acute leukemia
Colour segmentation
HSI colour space
K-means clustering
Linear contrast
spellingShingle Acute leukemia
Colour segmentation
HSI colour space
K-means clustering
Linear contrast
A. S., Abdul Nasir
Mohd Yusoff, Mashor, Prof. Dr.
H., Rosline
Unsupervised colour segmentation of white blood cell for acute leukaemia images
description Link to publisher's homepage at http://ieeexplore.ieee.org/
author2 aimi_salihah@yahoo.com
author_facet aimi_salihah@yahoo.com
A. S., Abdul Nasir
Mohd Yusoff, Mashor, Prof. Dr.
H., Rosline
format Working Paper
author A. S., Abdul Nasir
Mohd Yusoff, Mashor, Prof. Dr.
H., Rosline
author_sort A. S., Abdul Nasir
title Unsupervised colour segmentation of white blood cell for acute leukaemia images
title_short Unsupervised colour segmentation of white blood cell for acute leukaemia images
title_full Unsupervised colour segmentation of white blood cell for acute leukaemia images
title_fullStr Unsupervised colour segmentation of white blood cell for acute leukaemia images
title_full_unstemmed Unsupervised colour segmentation of white blood cell for acute leukaemia images
title_sort unsupervised colour segmentation of white blood cell for acute leukaemia images
publisher Institute of Electrical and Electronics Engineers (IEEE)
publishDate 2011
url http://dspace.unimap.edu.my/xmlui/handle/123456789/14075
_version_ 1643791133752426496
score 13.209306

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