Email this article 
Open Access
Subscription or Fee Access
Multi-type Classification of Mammogram Abnormalities by GHM and Multiclass SVM
Abstract
Cancer is a life-threatening disease, which consumes numerous human lives. However, the lifespan of the patients can be extended, when the disease is treated properly at the right time. This article renders a small contribution to the medical world for detecting the abnormalities in the mammograms. The research goal is attained by four important phases such as mammogram pre-processing, segmentation, feature extraction and classification. In the pre-processing phase, median filter is utilized to enhance the quality of an image. The pre-processed image is then segmented by FCM. The features are extracted from the image by Gaussian Hermite Moments, which are proven to be simple, efficient and noise resistant. Finally, multiclass SVM classifies between the normal, malignant and benign kinds of cancer. The performance of the proposed approach is evaluated against several analogous approaches in terms of accuracy, sensitivity and specificity. The proposed approach shows convincing results.
Keywords
References
http://icmr.nic.in/icmrsql/archive/2016/7.pdf
Gulsrud TO, Husoy JH: Optimal filter-based detection of microcalcifications. IEEE Trans Biomed Eng 48(11):1272–1281, 2001
Lee SK, Lo CS, Wang CM, Chung PC, Chang CI, Yang CW, Hsu PC: A computer-aided design mammography screening system for detection and classification of microcalcifications. Int J Med Inform 60(1):29–57, 2000
Nakayama R, Uchiyama Y, Yamamoto K, Watanabe R, Namba K (2006) Computer-aided diagnosis scheme using a filter bank for detection of microcalcification clusters in mammograms. IEEE Trans Biomed Eng 53: 273- 283.
Kavitha K, Kumaravel N (2007) A Comparative Study of Various Micro Calcification Cluster Detection Methods in Digitized Mammograms. Systems, Signals and Image Processing, 2007 and 6th EURASIP Conference focused on Speech and Image Processing, Multimedia Communications and Services, pp: 405-409.
Halkiotis S, Botsis T, Rangoussi M (2007) Automatic detection of clustered microcalcifications in digital mammograms using mathematical morphology and neural networks. Signal Processing 87: 1559-1568.
Bhattacharya M, Das A (2007) Fuzzy logic based segmentation of microcalcification in breast using digital mammograms considering multiresolution. Machine Vision and Image Processing Conference, pp: 98-105.
Pal NR, Bhowmick B, Patel SK, Pal S, Das J, et al. (2008) A multi-stage neural network aided system for detection of microcalcifications in digitized mammograms. Neurocomputing 71: 2625-2634.
Oh WV, Kim KG, Kim YJ, Kang H, Ro J, et al. (2009) Detection of microcalcifications in digital mammograms using foveal method. Journal of Korean Society of Medical Informatics 15: 165-172
Harirchi F, Radparvar P, Moghaddam HA, Toosi KN, Dehghan F, et al. (2010) Two-level algorithm for MCs detection in mammograms using DiverseAdaboost-SVM. Pattern Recognition (ICPR), 20th International Conference, pp: 269-272.
Oliver A, Torrent A, Lladó X, Tortajada M, Tortajada L, et al. (2012) Automatic microcalcification and cluster detection for digital and digitised mammograms. Knowledge-Based Systems 28: 68-75.
Zhang X, Gao X (2012) Twin support vector machines and subspace learning methods for microcalcification clusters detection. Engineering Applications of Artificial Intelligence 25: 1062-1072.
J. Shen, Orthogonal Gaussian–Hermite moments for image characterization, in: Proc. SPIE Intelligent Robots Computer Vision XVI: Algorithms, Techniques, Active Vision, and Materials Handling, vol. 3280, Pittsburgh, PA, USA, October 1997, pp. 224–233.
L. Wang, H. Sau, M. Dai, Fingerprint image segmentation based on Gaussian–Hermite moments, in: Lecture Notes in Computer Science, vol. 3584, 2005, pp. 446–454.
Lin Wang, Mo Dai, An effective method for extracting singular points in fingerprint images, Int. J. Electron. Commun. (AEU) 60 (9) (2006) 671–676.
Lin Wang, Mo Dai, Application of a new type of singular points in fingerprint classification, Pattern Recogn. Lett. 28 (2007) 1640–1650.
X. Zheng, M. Dai, M. Zhou, Gauss–Hermite moments application in medical image segmentation, in: 5th International Conference on Visual Information Engineering (VIE 2008), 2008, pp. 1–5.
S.M. Mahbubur Rahman, M. Omair Ahmad, M.N.S. Swamy, Bayesian wavelet based image denoising using the Gauss–Hermite expansion, IEEE Trans. Image Process. 17 (10) (2008) 1755–1771.
L. Ma, T.N. Tan, Y.H. Wang, D.X. Zhang, Local intensity variation analysis for iris recognition, Pattern Recogn. 37 (6) (2004) 1287–1298.
X. Ma, R. Pan, L. Wang, License plate character recognition based on Gaussian–Hermite moments, in: Proceedings of the Second International Workshop on Education Technology and Computer Science, vol. 3, 2010, pp. 11–14.
M. Abramowiz, I.A. Stegun, Handbook of Mathematical Functions, Dover Publications, Inc., New York, 1965.
Lin Wang, Youfu Wu, Mo Dai, Some aspects of Gaussian–Hermite moments in image analysis, in: IEEE Third International Conference on Natural Computation (ICNC), vol. 2, 2007, pp. 450–454.
Hsu, C-W. and Lin, C-J., ―A comparison of methods for multiclass support vector machines,‖ IEEE Trans. Neural Networks, vol. 13, pp. 415-425, 2002.
The new MIAS Database. Available: http://www.wiau.man.ac.uk/services/MIAS/MIASfaq.html.
Hosny, Khalid M. "Exact Legendre moment computation for gray level images." Pattern Recognition 40.12 (2007): 3597-3605.
Mukundan, Ramakrishnan, S. H. Ong, and Poh Aun Lee. "Image analysis by Tchebichef moments." IEEE Transactions on image Processing 10.9 (2001): 1357-1364.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.