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Automatic Segmentation of Acute Leukemia Cells

by A.H. Kandil, O. A. Hassan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 133 - Number 10
Year of Publication: 2016
Authors: A.H. Kandil, O. A. Hassan
10.5120/ijca2016907904

A.H. Kandil, O. A. Hassan . Automatic Segmentation of Acute Leukemia Cells. International Journal of Computer Applications. 133, 10 ( January 2016), 1-8. DOI=10.5120/ijca2016907904

@article{ 10.5120/ijca2016907904,
author = { A.H. Kandil, O. A. Hassan },
title = { Automatic Segmentation of Acute Leukemia Cells },
journal = { International Journal of Computer Applications },
issue_date = { January 2016 },
volume = { 133 },
number = { 10 },
month = { January },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume133/number10/23819-2016907904/ },
doi = { 10.5120/ijca2016907904 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:30:46.199479+05:30
%A A.H. Kandil
%A O. A. Hassan
%T Automatic Segmentation of Acute Leukemia Cells
%J International Journal of Computer Applications
%@ 0975-8887
%V 133
%N 10
%P 1-8
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The recognition of the acute Leukemia blast cells in colored microscopic images is a challenging task. Segmentation is the essential step for image analysis and image processing. In this paper, an algorithm is presented that consists of panel selection followed by segmentation using K-means clustering then a refinement process. This algorithm was applied on public dataset designed for testing segmentation techniques. The results were compared with two different segmentation techniques developed by other researchers on the same data set. Our algorithm results in a sensitivity of 97.4 % and specificity of 98.1%. The developed algorithm was tested to another dataset of samples extracted from patients in local hospitals. The algorithm results in sensitivity of 100%, Specificity of 99.747% and accuracy of 99.7617%. The results were approved by expert pathologists.

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Index Terms

Computer Science
Information Sciences

Keywords

Leukemia segmentation image enhancement K-means and watershed method.

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