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Leukemia Image Segmentation using K-Means Clustering and HSI Color Image Segmentation

by Mashiat Fatma, Jaya Sharma
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 94 - Number 12
Year of Publication: 2014
Authors: Mashiat Fatma, Jaya Sharma
10.5120/16393-6010

Mashiat Fatma, Jaya Sharma . Leukemia Image Segmentation using K-Means Clustering and HSI Color Image Segmentation. International Journal of Computer Applications. 94, 12 ( May 2014), 6-9. DOI=10.5120/16393-6010

@article{ 10.5120/16393-6010,
author = { Mashiat Fatma, Jaya Sharma },
title = { Leukemia Image Segmentation using K-Means Clustering and HSI Color Image Segmentation },
journal = { International Journal of Computer Applications },
issue_date = { May 2014 },
volume = { 94 },
number = { 12 },
month = { May },
year = { 2014 },
issn = { 0975-8887 },
pages = { 6-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume94/number12/16393-6010/ },
doi = { 10.5120/16393-6010 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:17:59.982794+05:30
%A Mashiat Fatma
%A Jaya Sharma
%T Leukemia Image Segmentation using K-Means Clustering and HSI Color Image Segmentation
%J International Journal of Computer Applications
%@ 0975-8887
%V 94
%N 12
%P 6-9
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

During the unfolding measures that are taken for the purpose of leukemia detection, segmentation of blood cells is a vital step. In this paper two approaches of such segmentation technique is proposed. While one uses K-means clustering, other uses color image based segmentation method. Both the processes segment the image into two regions, blasts & backgrounds. These blasts are our area of interest. The performance measure is based on the comparison of the two proposed techniques tends to find the more suitable approach for correct leukemia image segmentation. The results show that the segmentation based on K-means clustering gives better results preserving important information and removing background noise.

References
  1. Arthur R. Weeks, G. Eric Hague, "Color segmentation in the HSI color space using k-means algorithm", Univ. of Central Florida (USA).
  2. A. S. Abdul Nasir, M. Y. Mashor, H. Rosline, "Unsupervised Color Segmentation of White Blood Cell for Acute Leukemia Images".
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  5. A. N. A. Salihah, M. Y. Mashor, N . H. Harun, A. A. Abdullah, and H. Rosline, "Improving Colour Image Segmentation On Acute Myelogenous Leukemia Images Using Contrast Enhancement Techniques" in 2010 IEEE EMBS Conference on Biomedical Engineering & Sciences(IECBES 2010), Kuala Lumpur, Malaysia, 2010.
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  9. "Leukemia-Topic Overview". Retrieved August, 2013, http://www. webmed. com/cancer/tc/leukemia-topic-overview.
Index Terms

Computer Science
Information Sciences

Keywords

Leukemia k-means clustering blasts HSI color segmentation

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