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Colour image segmentation approach for detection of malaria parasites using various colour models and k-means clustering

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Main Authors: Aimi Salihah, Abdul-Nasir, Yusoff, Mashor, Prof. Dr., Zeehaida, Mohamed, Dr.
Other Authors: aimi_salihah@yahoo.com
Format: Article
Language:English
Published: World Scientific and Engineering Academy and Society (WSEAS) 2014
Subjects:
Colour models
Colour segmentation
K-means clustering
Malaria
Seeded region growing area extraction
Online Access:http://dspace.unimap.edu.my:80/dspace/handle/123456789/32393
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spelling my.unimap-323932014-03-06T07:50:46Z Colour image segmentation approach for detection of malaria parasites using various colour models and k-means clustering Aimi Salihah, Abdul-Nasir Yusoff, Mashor, Prof. Dr. Zeehaida, Mohamed, Dr. aimi_salihah@yahoo.com yusoff@unimap.edu.my zeehaida@kck.usm.my Colour models Colour segmentation K-means clustering Malaria Seeded region growing area extraction Link to publisher's homepage at http://www.wseas.org/ Malaria is a serious global health problem that is responsible for nearly one million deaths each year. With the large number of cases diagnosed over the year, rapid detection and accurate diagnosis of malaria infection which facilitates prompt treatment are essential to control malaria. This paper presents a color image segmentation approach for detection of malaria parasites that has been applied on malaria images of P. vivax species. In order to obtain the segmented red blood cells infected with malaria parasites, the images are first enhanced by using partial contrast stretching. Then, an unsupervised segmentation technique namely k-means clustering has been used to segment the infected cell from the background. Different colour components of RGB, HSI and C-Y colour models have been analysed to identify colour component that can give significant segmentation performance. Finally, median filter and seeded region growing area extraction algorithms have been applied for smoothing the image and remove any unwanted regions from the image, respectively. The proposed segmentation method has been evaluated on 100 malaria images. Overall, segmentation using S component of C-Y colour model has proven to be the best in segmenting the malaria image with segmentation accuracy and F-score of 99.46% and 0.9370, respectively. 2014-03-06T07:50:46Z 2014-03-06T07:50:46Z 2013-01 Article WSEAS Transactions on Biology and Biomedicine, vol. 10(1), 2013, pages 41-55 1109-9518 http://wseas.org/cms.action?id=6965 http://dspace.unimap.edu.my:80/dspace/handle/123456789/32393 en World Scientific and Engineering Academy and Society (WSEAS)
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 models
Colour segmentation
K-means clustering
Malaria
Seeded region growing area extraction
spellingShingle Colour models
Colour segmentation
K-means clustering
Malaria
Seeded region growing area extraction
Aimi Salihah, Abdul-Nasir
Yusoff, Mashor, Prof. Dr.
Zeehaida, Mohamed, Dr.
Colour image segmentation approach for detection of malaria parasites using various colour models and k-means clustering
description Link to publisher's homepage at http://www.wseas.org/
author2 aimi_salihah@yahoo.com
author_facet aimi_salihah@yahoo.com
Aimi Salihah, Abdul-Nasir
Yusoff, Mashor, Prof. Dr.
Zeehaida, Mohamed, Dr.
format Article
author Aimi Salihah, Abdul-Nasir
Yusoff, Mashor, Prof. Dr.
Zeehaida, Mohamed, Dr.
author_sort Aimi Salihah, Abdul-Nasir
title Colour image segmentation approach for detection of malaria parasites using various colour models and k-means clustering
title_short Colour image segmentation approach for detection of malaria parasites using various colour models and k-means clustering
title_full Colour image segmentation approach for detection of malaria parasites using various colour models and k-means clustering
title_fullStr Colour image segmentation approach for detection of malaria parasites using various colour models and k-means clustering
title_full_unstemmed Colour image segmentation approach for detection of malaria parasites using various colour models and k-means clustering
title_sort colour image segmentation approach for detection of malaria parasites using various colour models and k-means clustering
publisher World Scientific and Engineering Academy and Society (WSEAS)
publishDate 2014
url http://dspace.unimap.edu.my:80/dspace/handle/123456789/32393
_version_ 1643796882235850752
score 13.160551

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