Unsupervised segmentation technique for acute leukemia cells using clustering algorithms

Leukaemia is a blood cancer disease that contributes to the increment of mortality rate in Malaysia each year.There are two main categories for leukaemia, which are acute and chronic leukaemia.The production and development of acute leukaemia cells occurs rapidly and uncontrollable. Therefore, if t...

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Main Authors: Harun, Nor Hazlyna, Abdul Nasir, Aimi Salihah, Mashor, Mohd Yusoff, Hassan, Rosline
Format: Article
Language:English
Published: http://waset.org/publication/Unsupervised-Segmentation-Technique-for-Acute-Leukemia-Cells-Using-Clustering-Algorithms/10000450 2015
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Online Access:http://repo.uum.edu.my/17278/1/WASET%20253-259.pdf
http://repo.uum.edu.my/17278/
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spelling my.uum.repo.172782016-04-27T03:32:22Z http://repo.uum.edu.my/17278/ Unsupervised segmentation technique for acute leukemia cells using clustering algorithms Harun, Nor Hazlyna Abdul Nasir, Aimi Salihah Mashor, Mohd Yusoff Hassan, Rosline Q Science (General) Leukaemia is a blood cancer disease that contributes to the increment of mortality rate in Malaysia each year.There are two main categories for leukaemia, which are acute and chronic leukaemia.The production and development of acute leukaemia cells occurs rapidly and uncontrollable. Therefore, if the identification of acute leukaemia cells could be done fast and effectively, proper treatment and medicine could be delivered. Due to the requirement of prompt and accurate diagnosis of leukaemia, the current study has proposed unsupervised pixel segmentation based on clustering algorithm in order to obtain a fully segmented abnormal white blood cell (blast) in acute leukaemia image.In order to obtain the segmented blast, the current study proposed three clustering algorithms which are k-means, fuzzy c-means and moving k-means algorithms have been applied on the saturation component image. Then, median filter and seeded region growing area extraction algorithms have been applied, to smooth the region of segmented blast and to remove the large unwanted regions from the image, respectively.Comparisons among the three clustering algorithms are made in order to measure the performance of each clustering algorithm on segmenting the blast area. Based on the good sensitivity value that has been obtained, the results indicate that moving k-means clustering algorithm has successfully produced the fully segmented blast region in acute leukaemia image. Hence, indicating that the resultant images could be helpful to haematologists for further analysis of acute leukaemia. http://waset.org/publication/Unsupervised-Segmentation-Technique-for-Acute-Leukemia-Cells-Using-Clustering-Algorithms/10000450 2015 Article PeerReviewed application/pdf en http://repo.uum.edu.my/17278/1/WASET%20253-259.pdf Harun, Nor Hazlyna and Abdul Nasir, Aimi Salihah and Mashor, Mohd Yusoff and Hassan, Rosline (2015) Unsupervised segmentation technique for acute leukemia cells using clustering algorithms. International Journal of Computer, Control, Quantum and Information Engineering, 9 (1). pp. 253-259.
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutionali Repository
url_provider http://repo.uum.edu.my/
language English
topic Q Science (General)
spellingShingle Q Science (General)
Harun, Nor Hazlyna
Abdul Nasir, Aimi Salihah
Mashor, Mohd Yusoff
Hassan, Rosline
Unsupervised segmentation technique for acute leukemia cells using clustering algorithms
description Leukaemia is a blood cancer disease that contributes to the increment of mortality rate in Malaysia each year.There are two main categories for leukaemia, which are acute and chronic leukaemia.The production and development of acute leukaemia cells occurs rapidly and uncontrollable. Therefore, if the identification of acute leukaemia cells could be done fast and effectively, proper treatment and medicine could be delivered. Due to the requirement of prompt and accurate diagnosis of leukaemia, the current study has proposed unsupervised pixel segmentation based on clustering algorithm in order to obtain a fully segmented abnormal white blood cell (blast) in acute leukaemia image.In order to obtain the segmented blast, the current study proposed three clustering algorithms which are k-means, fuzzy c-means and moving k-means algorithms have been applied on the saturation component image. Then, median filter and seeded region growing area extraction algorithms have been applied, to smooth the region of segmented blast and to remove the large unwanted regions from the image, respectively.Comparisons among the three clustering algorithms are made in order to measure the performance of each clustering algorithm on segmenting the blast area. Based on the good sensitivity value that has been obtained, the results indicate that moving k-means clustering algorithm has successfully produced the fully segmented blast region in acute leukaemia image. Hence, indicating that the resultant images could be helpful to haematologists for further analysis of acute leukaemia.
format Article
author Harun, Nor Hazlyna
Abdul Nasir, Aimi Salihah
Mashor, Mohd Yusoff
Hassan, Rosline
author_facet Harun, Nor Hazlyna
Abdul Nasir, Aimi Salihah
Mashor, Mohd Yusoff
Hassan, Rosline
author_sort Harun, Nor Hazlyna
title Unsupervised segmentation technique for acute leukemia cells using clustering algorithms
title_short Unsupervised segmentation technique for acute leukemia cells using clustering algorithms
title_full Unsupervised segmentation technique for acute leukemia cells using clustering algorithms
title_fullStr Unsupervised segmentation technique for acute leukemia cells using clustering algorithms
title_full_unstemmed Unsupervised segmentation technique for acute leukemia cells using clustering algorithms
title_sort unsupervised segmentation technique for acute leukemia cells using clustering algorithms
publisher http://waset.org/publication/Unsupervised-Segmentation-Technique-for-Acute-Leukemia-Cells-Using-Clustering-Algorithms/10000450
publishDate 2015
url http://repo.uum.edu.my/17278/1/WASET%20253-259.pdf
http://repo.uum.edu.my/17278/
_version_ 1644282180584603648
score 13.18916