SURFACELET TRANSFORM BASED MAMMOGRAM CLASSIFICATION SYSTEM
DOI:
https://doi.org/10.29284/ijasis.2.1.2016.11-18Keywords:
Mammogram, CAD, Surfacelet Transform, Decision TreeAbstract
Computer Aided Diagnosis (CAD) system plays an important role in the medical field. It helps to reduce the mortality rate due to the early diagnosis of cancers. Photographing the changes in the internal breast structure due to the formation of masses and MicroCalcifications (MC) for the detection of breast cancer is known as mammography. It uses X-rays to capture the breast tissues. In this paper, the breast tumour in the mammogram is classified into benign or malignant classes using surfacelet transform. First, the Region Of Interest (ROI) is extracted and then enhanced using histogram equalization. The enhanced mammogram ROI is subjected to surfacelet transform and features are extracted using surfacelet coefficients. Then the features are fed to Decision Tree (DT) classifier for two class prediction; benign or malignant.
References
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S, Lahmiri, and M. Boukadoum, Hybrid discrete wavelet transform and Gabor filter banks processing for mammogram features extraction, IEEE 9th International New Circuits and Systems Conference, 2011, pp. 53-56.
S. Mohan Kumar, and G. Balakrishnan, Statistical Features Based Classification of Micro calcification in Digital Mammogram using Stochastic Neighbour Embedding, International Journal of Advanced Information Science and Technology, Vol. 7, No. 7, 2012, pp. 20-26.
S. Mohan Kumar, and G. Balakrishnan, The Performance Evaluation of the Breast Mass classification CAD System Based on DWT, SNE AND SVM, International Journal of Emerging Technology and Advanced Engineering, Vol. 3, No. 10, 2013, pp. 581-587.
S. Mohan Kumar, and G. Balakrishnan, Categorization of Benign And Malignant Digital Mammograms Using Mass Classification SNE and DWT, Karpagam Journal of Computer Science, Vol. 7, No. 4, 2013, pp. 237-243.
S. Mohan Kumar, and G. Balakrishnan, The Performance Evaluation of the Breast Microcalcification CAD System Based on DWT, SNE AND SVM, CiiT International Journal of Digital Image Processing, Vol. 5, No. 11, 2013, pp. 483- 487.
J. Suckling, The Mammographic Image Analysis Society Digital Mammogram Database, Exerpta Medica. International Congress Series 1069, 1994, pp. 375-378.
I. Kitanovski, B. Jankulovski, I. Dimitrovski, and S. Loskovska, Comparison of feature extraction algorithms for mammography images, IEEE 4th International Congress on Image and Signal Processing, Vol. 2, 2011, pp. 888-892.
M.E. Elmanna, and Y.M. Kadah, Implementation of practical computer aided diagnosis system for classification of masses in digital mammograms, IEEE International Conference on Computing, Control, Networking, Electronics and Embedded Systems Engineering, 2015, pp. 336-341.
S. Xu, and C. Pei, Hierarchical matching for automatic detection of masses in mammograms, IEEE International Conference on Electrical and Control Engineering, 2011, pp. 4523-4526.
B.C. Patel, and G.R. Sinha, Mammography feature analysis and mass detection in breast cancer images, IEEE International Conference on Electronic Systems, Signal Processing and Computing Technologies, 2014, pp. 474-478.
Y. Sun, C.F. Babbs, and E.J. Delp, A comparison of feature selection methods for the detection of breast cancers in mammograms: adaptive sequential floating search vs. genetic algorithm, IEEE 27th Annual International Conference of the Engineering in Medicine and Biology Society, 2005, pp. 6532-6535.
A. Cao, Q. Song, X. Yang, S. Liu, and C. Guo, Mammographic mass detection by vicinal support vector machine, IEEE International Joint Conference on Neural Networks, Vol. 3, 2004, pp. 1953-1958.
M.V.S. de Cea, and Y. Yang, Case-adaptive decision rule for detection of clustered microcalcifications in mammograms, IEEE 12th International Symposium on Biomedical Imaging, 2015, pp. 1147-1150.
L. Zhang, and X. Gao, Research on translation-invariant wavelet transform for classification in mammograms, IEEE 3rd International Conference on Natural Computation, Vol. 3, 2007, pp. 571-575.
I. Faye, B.B. Samir, and M.M. Eltoukhy, Digital mammograms classification using a wavelet based feature extraction method, IEEE Second International Conference on Computer and Electrical Engineering, Vol. 2, 2009, pp. 318-322.
A. Tirtajaya, and D.D. Santika, Classification of microcalcification using dual-tree complex wavelet transform and support vector machine, IEEE 2nd International Conference on Advances in Computing, Control and Telecommunication Technologies, 2010, pp. 164-166.
S, Lahmiri, and M. Boukadoum, Hybrid discrete wavelet transform and Gabor filter banks processing for mammogram features extraction, IEEE 9th International New Circuits and Systems Conference, 2011, pp. 53-56.
S. Mohan Kumar, and G. Balakrishnan, Statistical Features Based Classification of Micro calcification in Digital Mammogram using Stochastic Neighbour Embedding, International Journal of Advanced Information Science and Technology, Vol. 7, No. 7, 2012, pp. 20-26.
S. Mohan Kumar, and G. Balakrishnan, The Performance Evaluation of the Breast Mass classification CAD System Based on DWT, SNE AND SVM, International Journal of Emerging Technology and Advanced Engineering, Vol. 3, No. 10, 2013, pp. 581-587.
S. Mohan Kumar, and G. Balakrishnan, Categorization of Benign And Malignant Digital Mammograms Using Mass Classification SNE and DWT, Karpagam Journal of Computer Science, Vol. 7, No. 4, 2013, pp. 237-243.
S. Mohan Kumar, and G. Balakrishnan, The Performance Evaluation of the Breast Microcalcification CAD System Based on DWT, SNE AND SVM, CiiT International Journal of Digital Image Processing, Vol. 5, No. 11, 2013, pp. 483- 487.
J. Suckling, The Mammographic Image Analysis Society Digital Mammogram Database, Exerpta Medica. International Congress Series 1069, 1994, pp. 375-378.
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Published
2016-06-30
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J S, L. J. . (2016). SURFACELET TRANSFORM BASED MAMMOGRAM CLASSIFICATION SYSTEM. INTERNATIONAL JOURNAL OF ADVANCES IN SIGNAL AND IMAGE SCIENCES, 2(1), 11–18. https://doi.org/10.29284/ijasis.2.1.2016.11-18
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