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

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International Journal of Computer Applications
© 2011 by IJCA Journal
Number 1 - Article 1
Year of Publication: 2011
Authors:
Imad Zyout
Ikhlas Abdel-Qader
10.5120/3798-5235

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

@article{key:article,
	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}
}

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.

Reference

  • Elter, M. and Horsch, A. 2009. CADx of mammographic mass and clustered micro-calcifications: A review. Medical Physics, 36(6), 2052-2068.
  • Zyout, I. 2010. Toward automated detection and diagnosis of mammographic microcalcifications. Doctoral dissertation, Dept. of Elect. & Comp. Eng., Western Michigan University.
  • Dhawan, A. P., Chitre, Y., Bonasso, C., and Wheele, K. 1995. Radial-basis-function-based classification of mammographic microcalcifications using texture features. In Proceedings of the 17th Annual International Conference and 21st Canadian Medical and Biological Engineering Conference, 535–536.
  • Chan, H. P., Sahiner, B., Lam, K. L., Petrick, N., Helvie, M. A., Goodsitt, M. M., and Adler, D. D. 1998. Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces. Medical Physics, 2007–2019.
  • Zadeh, H. S., Nezhad, P.S., and Rad, F. R. 2001. Shape based and texture-based feature extraction for classification of microcalcifications in mammograms. In Proceedings of SPIE Medical Imaging, 4322, 3010-310.
  • Zadeh, H. S., Rad, F. R., and Nejad, S. P. 2004. Comparison of multiwavelet, wavelet, Haralick, and shape features for microcalcification classification in mammograms. Pattern Recognition, 37, 1973-1986.
  • Singh, S., Kumar, V., Verma, H. K., and Singh, D. 2006. SVM Based System for classification of Microcalcifications in Digital Mammograms. In proceeding of the 28th IEEE EMBS Annual International Conference, New York City, USA.
  • Hamdi, N., Auhmani, K., and Hassani, M. M. 2008. Design of a high-accuracy classifier based on fisher discriminate analysis: Application to Computer-Aided Diagnosis of Microcalcifications. In Proceedings of the International Conference on Computational Sciences and its Applications ( ICCSA 2008).
  • Karahaliou, A., Boniatis, I., Sakellaropoulos,P., Skiadopoulos, S., Panayiotakis, G., and Costaridou, L. 2007. Can texture of tissue surrounding microcalcifica-tions in mammography be used for breast cancer diagnosis? Nuclear Instruments and Methods in Physics Research, 580, 1071–1074.
  • Thiele, D. L., Kimme-Smith, C., Johnson, T. D., McCombs, M., and Bassett, L. W. 1996. Using tissue texture surrounding calcification clusters to predict benign vs malignant outcomes. Medical Physics, 23, 549-555.
  • Guo, X. C. , Yang, J. H., Wu, G. C., Wang, C. Y., and Liang, Y. C. 2008. A novel LS-SVMs hyper- parameter selection based on particle swarm optimization. Neurocomputing, 71, 3211– 3215.
  • Haralick, R. M. 1979. Statistical and structural approaches to texture. In Proceedings of IEEE, 67 (5), 786–804.
  • Kennedy, J. and Eberhart, R. 1995. Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural Networks, Perth: IEEE Service Center, Piscataway, NJ, 4, 1942–1948.
  • Siedlecki, W. and Sklansky, J. 1989. A note on genetic algorithm for large scale feature selection. Pattern recognition letter, 10, 335-347.
  • Kennedy, J. and Eberhart, R. C. 1997. A discrete binary version of the particle swarm algorithm. In Proceedings of the Conference on Systems, Man, and Cybernetics, Piscataway, NJ, 4104-4109.
  • Suckling, J., Parker, J., Dance, D., Astley, S., Hutt, I., Boggis, C., Ricketts, I., Stamatakis, E., Cerneaz, N., Kok, S., Taylor, P., Betal, D., and Savage, J. 1994. The mammographic image analysis society digital mammogram database. Exerpta Medica, 1069, 375-378.
  • Escalante, H. J., Montes, M., and Sucar, L. E. 2009. Particle Swarm Model Selection. Journal of Machine Learning Research, 10, 405-440.
  • Papadopoulos, A., Fotiadis, D. I., and Likas, A. 2005. Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines. Artificial Intelligence in Medicine, 4( 2),141-150.