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