Combining Feature Methods for Content-Based Classification of Mammogram Images
Keywords:
mammogram, classification, Gabor filters, Grey Level Co-occurrence Matrix, Haralick Features.Abstract
Breast cancer is among the leading cause of death among females. Studies show that early detection allows for a better prognosis. Mammography is one of the successful ways for early detection of breast cancer. It mostly involves manual reading of mammograms, a process that is difficult and error-prone. This paper discusses a classification model for mammograms based on microcalcification characteristics, as a way of helping radiologists make quick and accurate diagnostic decisions by availing to them similar past cases. The images are pre-processed by Gaussian smoothing and median filtering with 5 x 5 and 3 x 3 kernels respectively. Gabor and Haralick features are then extracted to form the image signatures over which similarity measurements are made. Experimental results show an average precision value between 0.5 and 0.61 using Haralick features, 0.49 and 0.57 using Gabor features, and 0.51 and 0.78 using combination of Gabor and Haralick features.References
R. M. Rangayyan, Breast cancer and mammography, in: Biomedical Image Analysis, Springer-verlag, 2005, pp. 22-27.
A. Oliver, J. Freixenet, R. Marti, R. Zwiggelaar, A comparison of breast tissue classification techniques, in: MICCAI, CRC Press, 2006, pp. 872-879.
S. Z. Hamid, R. R. Farshid, P. N. Siamak, Comparison of multiwavelet, wavelet, haralick, and shape features for microcalcification classification in mammograms, Pattern Recognition 37 (2004) 1973-1986. http://dx.doi.org/10.1016/j.patcog.2003.03.001
C. T. Li, C. H. Wei, C. H. Wei, C. Li, A content-based approach to medical image database retrieval, Database Modeling for Industrial Data Management: Emerging Technologies and Applications 10 (6) (2005) 681-685.
H. Muller, N. Michoux, D. Bandon, A. Geissbuhler, A review of content-based image retrieval systems, in: Medical applications-clinical benefits and future directions, Int J Med Inform, 2004, pp. 1-23.
E. Miyamoto, T. Merryman, Fast calculation of haralick texture features, Tech. rep., Carnegie Mellon University.
L. O. Martins, G. Junior, A. C. Silva, A. Palva, M. Gatas, Detection of masses in digital mammograms using k-means and support vector machine, Electronic Letters on Computer Vision and Image Analysis 8 (2) (2009) 39-50.
C. H. Wei, L. Chang-Tsun, W. Roland, A content-based approach to medical image database retrieva, Database Technologies: Concepts, Methodologies, Tools, and Applications (2009) 1062-1083.
R. M. Haralick, K. Shanmugan, I. Dinstein, Textural features for image classification, IEEE Transactions on systems, man, and Cybernetics SMC-3 (6) (1973) 610 -621. http://dx.doi.org/10.1109/TSMC.1973.4309314
S. K. e. a. Lee, A computer aided design mammography screening system for detection and classification of microcalcifications, International journal of medical informatics 60 (2000) 29-57. http://dx.doi.org/10.1016/S1386-5056(00)00067-8
T. S. Lee, Image representation using 2d gabor wavelets, IEEE Transactions on Pattern Analysis and Machine Intelligence 10 (1996) 959-971.
M. R. Turner, Texture discrimination by gabor functions, Biological Cybernetics 10 (1986) 71-82.
Y. Hamamoto, S. Uchimura, M. Watanabe, Y. T., S. Tomita, A gabor filter-based method for recognizing handwritten numerals, Pattern Recognition 31 (4) (1998) 395-400. http://dx.doi.org/10.1016/S0031-3203(97)00057-5
P. Kruizinga, P. Nicolai, E. G. Simona, Comparison of texture features based on gabor filters, in: ICIAP, 1999, pp. 142-147.
C. Liu, H. Wechsler, Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition, IEEE Trans. Image Processing 11 (4) (2002) 467- 476. http://dx.doi.org/10.1109/TIP.2002.999679
V. Kyrki, J. Kamarainen, H. Kalviainen, Simple gabor feature space for invariant object recognition, Pattern Recognition Letters 10 (2004) 311-318. http://dx.doi.org/10.1016/j.patrec.2003.10.008
A. Dimai, Rotation invariant texture description using general moment invariants and gabor filters, in: Proc. of the 11th Scandinavian conference on Image analysis, 1999, pp. 391-398.
M. Steinbach, P.-N. Tan, kNN: k-Nearest Neighors, Data Mining and Knowledge Discovery, Taylor Francis Group, 2009, Ch. 8, pp. 151-161.
J. Suckling, J. Parker, D. R. Dance, S. Astley, I. Hutt, C. Boggis, I. Ricketts, E. Stamatakis, N. Cerneaz, S. L. Kok, P. Taylor, D. Betal, J. Savage, The mammographic image analysis society digital mammogram database, Exerpta Medica. International Congress Series 1069 (1994) 375-378.
J. Luo, M. A. Nascimento, Content-based sub-image retrieval using relevance feedback, in: Proc. of MMDB, 2004, pp. 2-9. http://dx.doi.org/10.1145/1032604.1032607
C. H. Wei, Y. Li, C. T. Li., Effective extraction of gabor features for adaptive mammogram retrieval, in: Proc. of ICM, 2007, pp. 1503-1506.
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