A Comparative Study of Breast Mass Classification based on Spherical Wavelet Transform using ANN and KNN Classifiers

Year 2012, Volume: 2 Issue: 1, 79 - 85, 01.06.2012

Pelin Görgel Ahmet Sertbas Osman Nuri Ucan

Abstract

Breast cancer may be missed by the radiologists at the early ages because of the mammography artifacts. Computer aided diagnosis can decrease the mortality rate by providing a second eye. The artifacts exist due to the noise and the inappropriate contrast in mammograms. Therefore a study that classifies the cropped region of interests (ROI’s) as benign or malign and provides a second eye to the radiologists is proposed. The study consists of two steps: First step is the application of Spherical Wavelet Transform (SWT) to the original ROI matrix prior to feature extraction. Second step is to extract some predetermined pixel and shape features both from wavelet and scaling coefficients. Finally, for classification the prepared feature matrix is given to Artificial Neural Networks (ANN) and K-Nearest Neighbour (KNN) systems which are widely used in image processing. The algorithms are tested on 60 abnormal digitized mammogram ROIs acquised from The Mammographic Image Analysis Society (MIAS) which is a free mammogram database

Keywords

Breast cancer, tumor classification, Spherical Wavelet Transform (SWT), ANN, K-Nearest Neighbour

References

• Abrial,
• P & Moudden, Y 2007,
• "Morphological component analysis and
• inpainting on the sphere: application in
• physics and astrophysics" , Journal of
• Fourier Analysis and Applications (JFAA),
• special issue on ``Analysis on the Sphere'',
• Vol. 13, pp. 729-748.
• Ali, F, Eldokany, I, Saad, A and Abdelsamie, F 2008, “Curvelet fusion of MR and CT images”, Progress in Electromagnitics Research, pp. 215–224.
• Binh, N & Thanh, C 2007, “Object detection of speckle image base on curvelet transform”, ARPN Journal of Engineering and Applied Sciences, Vol. 2, pp. 14-16.
• Donoho, D & Duncan, M 2000, in Proc. Aerosense 2000, Wavelet Applications VII, ed. H. Szu, M. Vetterli, W. Campbell, & J. Buss, SPIE, 4056, 12.
• Eltoukhy, M, Faye, I & Samir, B 2010, “A comparison of wavelet and curvelet for breast cancer diagnosis in digital mammogram, Computers in Biology and Medicine, Vol. 40, pp. 384–391.
• Hwang, H & Choi, H 2005, “Classification of breast tissue images based on wavelet transform using discriminant analysis, neural network and SVM.”, IEEE, pp. 345-349.
• Karahaliou, N, A & Boniatis, I, S 2008, “Breast cancer diagnosis: analyzing texture of tissue surrounding microcalcifications”, IEEE Transactions on Information Technology in Biomedicine, Vol. 12, pp. 731-738.
• Panigrahi, B, K & Pandi, V, R 2009, “Optimal feature selection for classification of power quality disturbances using wavelet packet-based fuzzy k-nearest neighbour algorithm “, Generation, Distribution,Vol. 3, pp. 296 – 306. &
• Starck, J, Candès, E & Donoho, D, L 2003, "Astronomical image representation by the curvelet transform" , Astronomy and Astrophysics, pp. 785-800.
• W.R. Wade, “A Walsh System for Polar Coordinates”, Computers Math. Applic., Vol. 30, pp. 221-227, 1995.