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Article:Classification of Microcalcification Clusters via PSO-KNN Heuristic Parameter Selection and GLCM Features

by Imad Zyout, PhD, Ikhlas Abdel-Qader, PhD,PE
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 31 - Number 2
Year of Publication: 2011
Authors: Imad Zyout, PhD, Ikhlas Abdel-Qader, PhD,PE
10.5120/3798-5235

Imad Zyout, PhD, Ikhlas Abdel-Qader, PhD,PE . Article:Classification of Microcalcification Clusters via PSO-KNN Heuristic Parameter Selection and GLCM Features. International Journal of Computer Applications. 31, 2 ( October 2011), 34-39. DOI=10.5120/3798-5235

@article{ 10.5120/3798-5235,
author = { Imad Zyout, PhD, Ikhlas Abdel-Qader, PhD,PE },
title = { Article:Classification of Microcalcification Clusters via PSO-KNN Heuristic Parameter Selection and GLCM Features },
journal = { International Journal of Computer Applications },
issue_date = { October 2011 },
volume = { 31 },
number = { 2 },
month = { October },
year = { 2011 },
issn = { 0975-8887 },
pages = { 34-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume31/number2/3798-5235/ },
doi = { 10.5120/3798-5235 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:17:06.202909+05:30
%A Imad Zyout
%A PhD
%A Ikhlas Abdel-Qader
%A PhD,PE
%T Article:Classification of Microcalcification Clusters via PSO-KNN Heuristic Parameter Selection and GLCM Features
%J International Journal of Computer Applications
%@ 0975-8887
%V 31
%N 2
%P 34-39
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Texture-based computer-aided diagnosis (CADx) of microcalcification clusters is more robust than the state-of-art shape-based CADx because the performance of shape-based approach heavily depends on the effectiveness of microcalcification (MC) segmentation. This paper presents a texture-based CADx that consists of two stages. The first one characterizes MC clusters using texture features from gray-level co-occurrence matrix (GLCM). In the second stage, an embedded feature selection based on particle swarm optimization and a k-nearest neighbor (KNN) classifier, called PSO-KNN, is applied to simultaneously determine the most discriminative GLCM features and to find the best k value for a KNN classifier. Testing the proposed CADx using 25 MC clusters from mini-MIAS dataset produced classification accuracy of 88% that obtained using 2 GLCM features.

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Index Terms

Computer Science
Information Sciences

Keywords

Microcalcifications GLCM texture features feature selection particle swarm optimization