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

International Journal of Computer Applications
© 2011 by IJCA Journal
Number 1 - Article 1
Year of Publication: 2011
Imad Zyout
Ikhlas Abdel-Qader

PhD Imad Zyout and PE Ikhlas Abdel-Qader PhD. Article:Classification of Microcalcification Clusters via PSO-KNN Heuristic Parameter Selection and GLCM Features. International Journal of Computer Applications 31(2):34-39, October 2011. Full text available. BibTeX

	author = {Imad Zyout, PhD and 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},
	year = {2011},
	volume = {31},
	number = {2},
	pages = {34-39},
	month = {October},
	note = {Full text available}


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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