Comparison of Statistical, LBP, and Multi-Resolution Analysis Features for Breast Mass Classification
详细信息 查看全文
- 作者：Yasser A. Reyad (1)
Mohamed A. Berbar (2)
Muhammad Hussain (3) - 关键词：Mammographic images ; Breast cancer detection ; Wavelets ; Contourlet ; Support vector machine ; Local binary pattern ; Statistical features
- 刊名：Journal of Medical Systems
- 出版年：2014
- 出版时间：September 2014
- 年：2014
- 卷：38
- 期：9
- 全文大小：4,222 KB
- 参考文献：1. Moayedi, F., Azimifar, Z., Boostani, R., and Katebi, S., Contourlet-based mammography mass classification using the SVM family. / Comput. Biol. Med. 40(4):373-83, 2010. CrossRef
2. American Cancer Society, / Cancer Facts and Figures 2014. American Cancer Society, Atlanta, 2014. Available at http://www.cancer.org.
3. Sharaf-El-Deen, D. A., Moawad, I. F., and Khalifa, M. E., A new hybrid case-based reasoning approach for medical diagnosis systems. / J. Med. Syst. 38(2):1-1, 2014. CrossRef
4. Anderson, W. F., Jatoi, I., and Devesa, S. S., Assessing the impact of screening mammography: breast cancer incidence and mortality rates in Connecticut (1943-002). / Breast Cancer Res. Treat. 99(3):333-40, 2006. CrossRef
5. Yu, S.-N., and Huang, Y.-K., Detection of microcalcifications in digital mammograms using combined model-based and statistical textural features. / Expert Syst. Appl. 37(7):5461-469, 2010. CrossRef
6. Niwas, S. I., Palanisamy, P., Chibbar, R., and Zhang, W. J., An expert support system for breast cancer diagnosis using color wavelet features. / J. Med. Syst. 36(5):3091-102, 2012. CrossRef
7. Cheng, H. D., Cai, X., Chen, X., Hu, L., and Lou, X., Computer-aided detection and classification of microcalcifications in mammograms: a survey. / Pattern Recogn. 36(12):2967-991, 2003. CrossRef
8. Xianchuan, X., and Qi, Z., “Medical Image Retrieval Using Local Binary Patterns with Image Euclidean Distance,-in International Conference on Information Engineering and Computer Science, 2009. ICIECS 2009, 2009, pp. 1-.
9. Niwas, S. I., Palanisamy, P., and K. Sujathan, “Wavelet based feature extraction method for breast cancer cytology images,-in 2010 I.E. Symposium on Industrial Electronics Applications (ISIEA), 2010, pp. 686-90.
10. Li, J.-B., Mammographic image based breast tissue classification with kernel self-optimized fisher discriminant for breast cancer diagnosis. / J. Med. Syst. 36(4):2235-244, 2012. CrossRef
11. Lahmirim, S., and Boukadoum, M., “DWT and RT-based approach for feature extraction and classification of mammograms with SVM,-in 2011 I.E. Biomedical Circuits and Systems Conference (BioCAS), 2011, pp. 412-15.
12. Dheeba, J., and Selvi, S. T., A swarm optimized neural network system for classification of microcalcification in mammograms. / J. Med. Syst. 36(5):3051-061, 2012. CrossRef
13. Naghibi, S., Teshnehlab, M., and Shoorehdeli, M. A., Breast cancer classification based on advanced multi dimensional fuzzy neural network. / J. Med. Syst. 36(5):2713-720, 2012. CrossRef
14. Dheeba, J., and Selvi, S. T., An improved decision support system for detection of lesions in mammograms using differential evolution optimized wavelet neural network. / J. Med. Syst. 36(5):3223-232, 2012. CrossRef
15. Suganthi, M., and Madheswaran, M., An improved medical decision support system to identify the breast cancer using mammogram. / J. Med. Syst. 36(1):79-1, 2012. CrossRef
16. Kilic, N., Gorgel, P., Ucan, O. N., and Sertbas, A., Mammographic mass detection using wavelets as input to neural networks. / J. Med. Syst. 34(6):1083-088, 2010. CrossRef
17. Yaneli, A.-A. M., Nicandro, C.-R., Efrén, M.-M., Enrique, M.-D.-C.-M., Nancy, P.-C., and Gabriel, A.-M. H., Assessment of Bayesian network classifiers as tools for discriminating breast cancer pre-diagnosis based on three diagnostic methods. In: Batyrshin, I., and Mendoza, M. G. (Eds.), / Advances in artificial intelligence. Springer, Berlin, pp. 419-31, 2013. CrossRef
18. Domínguez, R., and Nandi, A. K., Toward breast cancer diagnosis based on automated segmentation of masses in mammograms. / Pattern Recogn. 42(6):1138-148, 2009. CrossRef
19. Huang, M.-L., Hung, Y.-H., Lee, W.-M., Li, R. K., and Wang, T.-H., Usage of case-based reasoning, neural network and adaptive neuro-fuzzy inference system classification techniques in breast cancer dataset classification diagnosis. / J. Med. Syst. 36(2):407-14, 2012. CrossRef
20. Hussain, M., Wajid, S. K., Elzaart, A., and Berbar, M., “A Comparison of SVM Kernel Functions for Breast Cancer Detection,-in 2011 Eighth International Conference on Computer Graphics, Imaging and Visualization (CGIV), 2011, pp. 145-50.
21. Sheshadri, H. S., and Kandaswamy, A., Experimental investigation on breast tissue classification based on statistical feature extraction of mammograms. / Comput. Med. Imaging Graph. 31(1):46-8, 2007. CrossRef
22. Ojala, T., Pietikainen, M., and Maenpaa, T., Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. / IEEE Trans. Pattern Anal. Mach. Intell. 24(7):971-87, 2002. CrossRef
23. Ojala, T., Pietik?inen, M., and Harwood, D., A comparative study of texture measures with classification based on featured distributions. / Pattern Recogn. 29(1):51-9, 1996. CrossRef
24. Fehr, J., and Burkhardt, H., -D rotation invariant local binary patterns,-in 19th International Conference on Pattern Recognition, 2008. ICPR 2008, 2008, pp. 1-.
25. Buciu, I., and Gacsadi, A., “Gabor wavelet based features for medical image analysis and classification,-in 2nd International Symposium on Applied Sciences in Biomedical and Communication Technologies, 2009. ISABEL 2009, 2009, pp. 1-.
26. Do, M. N., and Vetterli, M., The contourlet transform: an efficient directional multiresolution image representation. / IEEE Trans. Image Process. 14(12):2091-106, 2005. CrossRef
27. Burt, P. J., and Adelson, E. H., The Laplacian pyramid as a compact image code. / IEEE Trans. Commun. 31(4):532-40, 1983. CrossRef
28. Po, D.D. Y., and Do, M. N., “Directional multiscale modeling of images using the contourlet transform,-IEEE Trans. on Image Processing, to appear, Jun. 2006.
29. Issac Niwas, S., Palanisamy, P., Zhang, W. J., Isa, N. A. M., and Chibbar, R., “Log-gabor wavelets based breast carcinoma classification using least square support vector machine,-in 2011 I.E. International Conference on Imaging Systems and Techniques (IST), 2011, pp. 219-23.
30. Gharekhan, A. H., Arora, S., Panigrahi, P. K., and Pradhan, A., Distinguishing cancer and normal breast tissue autofluorescence using continuous wavelet transform. / IEEE J. Sel. Top. Quant. Electron. 16(4):893-99, 2010. CrossRef
31. Moayedi, F., Azimifar, Z., Boostani, R., and Katebi, S., Contourlet-based mammography mass classification. In: Kamel, M., and Campilho, A. (Eds.), / Image analysis and recognition. Springer, Berlin, pp. 923-34, 2007. CrossRef
32. Nunes, A. P., Silva, A. C., and Paiva, A. C., “Detection of Masses in Mammographic Images Using Simpson’s Diversity Index in Circular Regions and SVM,-in Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition, Berlin, Heidelberg, 2009, pp. 540-53.
33. de O Martins, L., Silva, A. C., de Paiva, A. C., and Gattass, M., Detection of breast masses in mammogram images using growing neural Gas algorithm and Ripley’s K function. / J. Sign. Process Syst. Sign. Image Video Technol. 55(1-):77-0, 2009. CrossRef
34. Gao, X., Wang, Y., Li, X., and Tao, D., On combining morphological component analysis and concentric morphology model for mammographic mass detection. / IEEE Trans. Inf. Technol. Biomed. 14(2):266-73, 2010. CrossRef
35. Terada, T., Fukumizu, Y., Yamauchi, H., Chou, H., and Kurumi, Y., “Detecting mass and its region in mammograms using mean shift segmentation and Iris Filter,-in 2010 International Symposium on Communications and Information Technologies (ISCIT), 2010, pp. 1176-179.
36. Ericeira, D. R., Silva, A. C., de Paiva, A. C., and Gattass, M., Detection of masses based on asymmetric regions of digital bilateral mammograms using spatial description with variogram and cross-variogram functions. / Comput. Biol. Med. 43(8):987-99, 2013. CrossRef
37. Agrawal, P., Vatsa, M., and Singh, R., Saliency based mass detection from screening mammograms. / Signal Process. 99:29-7, 2014. CrossRef
38. Pereira, D. C., Ramos, R. P., and do Nascimento, M. Z., Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm. / Comput. Methods Programs Biomed. 114(1):88-01, 2014. CrossRef
39. Dong, A., and Wang, B., “Feature selection and analysis on mammogram classification,-in IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, 2009. PacRim 2009, 2009, pp. 731-35. - 作者单位：Yasser A. Reyad (1)
Mohamed A. Berbar (2)
Muhammad Hussain (3)
1. Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Kingdom of Saudi Arabia
2. Department of Computer Engineering and Sciences, Faculty of Electronic Engineering, Menoufia University, Menoufia, Egypt
3. Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Kingdom of Saudi Arabia - ISSN：1573-689X
文摘
Millions of women are suffering from breast cancer, which can be treated effectively if it is detected early. Mammography is broadly recognized as an effective imaging modality for the early detection of breast cancer. Computer-aided diagnosis (CAD) systems are very helpful for radiologists in detecting and diagnosing abnormalities earlier and faster than traditional screening programs. An important step of a CAD system is feature extraction. This research gives a comprehensive study of the effects of different features to be used in a CAD system for the classification of masses. The features are extracted using local binary pattern (LBP), which is a texture descriptor, statistical measures, and multi-resolution frameworks. Statistical and LBP features are extracted from each region of interest (ROI), taken from mammogram images, after dividing it into N×N blocks. The multi-resolution features are based on discrete wavelet transform (DWT) and contourlet transform (CT). In multi-resolution analysis, ROIs are decomposed into low sub-band and high sub-bands at different resolution levels and the coefficients of the low sub-band at the last level are taken as features. Support vector machines (SVM) is used for classification. The evaluation is performed using Digital Database for Screening Mammography (DDSM) database. An accuracy of 98.43 is obtained using statistical or LBP features but when both these types of features are fused, the accuracy is increased to 98.63. The CT features achieved classification accuracy of 98.43 whereas the accuracy resulted from DWT features is 96.93. The statistical analysis and ROC curves show that methods based on LBP, statistical measures and CT performs equally well and they not only outperform DWT based method but also other existing methods.