ABSTRACT
Breast tissue density is one of the symptoms for breast cancer detection. Fully automatic breast tissue density classification is presented in this work. Present work consists of four steps which include breast region extraction and enhancement of mammograms, segmentation, feature extraction, and breast tissue density classification. Enhancement of mammogram is done by applying fractional order differential based filter. Segmentation of breast tissue segmentation has been done by using clustering based fast fuzzy c-means technique. Further, texture based local binary pattern (LBP) and dominant rotated local binary pattern (DRLBP) features have been computed from the extracted breast tissues to characterize its texture property. Support vector machine with linear kernel functions are used to classify the breast tissue density. Proposed algorithm is validated on the publicly available 322 mammograms of Mini-Mammographic Image Analysis Society (MIAS).
- Wolfe, J. N. 1976. Breast patterns as an index of risk for developing breast cancer. American Journal of Roentgenology, 126(1976), 1130--1137.Google ScholarCross Ref
- Ho, W. T. and Lam, P. W. T. 2003. Clinical performance of computer-assisted detection (CAD) system in detecting carcinoma in breasts of different densities. Clinical Radiology, 58(2003), 133--136.Google ScholarCross Ref
- Subashini, T.S., Ramalingam, V. and Palanivel, S. 2010. Automated assessment of breast tissue density in digital mammograms. Computer Vision and Image Understanding, 114(2010), 33--43. Google ScholarDigital Library
- Muštra, M., GrgićM., Delač K. 2012. Breast density classification using multiple feature selection. AUTOMATIKA: časopiszaautomatiku, mjerenje, elektroniku, računarstvo i komunikacije, 53(2012), 362--372.Google Scholar
- Vállez, N., Bueno, G., Déniz, O. Dorado, J., Seoane, J. A., Pazos, A. and Pastor, C. 2014. Breast density classification to reduce false positives in CADe systems. Computer methods and programs in biomedicine, 113(2014), 569--584. Google ScholarDigital Library
- Sharma, V. and Singh, S. 2014. CFS--SMO based classification of breast density using multiple texture models. Medical & biological engineering & computing, 52(2014), 521--529.Google Scholar
- Chen, Z., Denton, E., Zwiggelaar, R. 2013. Local Feature Based Breast Tissue Appearance Modelling for Mammographic Risk Assessment Annals of the BMVA, 2013, 1--19.Google Scholar
- Tzikopoulos, S.D., Mavroforakis, M.E., Georgiou, H.V., Dimitropoulos, N. and Theodoridis, S. 2011. A fully automated scheme for mammographic segmentation and classification based on breast density and asymmetry. computer methods and programs in biomedicine, 102(2011), 47--63. Google ScholarDigital Library
- Kashyap, K. L., Bajpai, M. K. and Khanna P. 2017. An efficient algorithm for mass detection and shape analysis of different masses present in digital mammograms. Multimedia Tools and Applications, 2017, 1--21. Google ScholarDigital Library
- Singh, K.K., Bajpai, M.K., Pandey, R.K. 2015. A novel approach for edge detection of low contrast satellite images. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(2015), 211--217.Google Scholar
- Singh, K. K., Bajpai, M. K., Pandey, R. K., and Munshi, P. 2016. A novel non-invasive method for extraction of geometrical and texture features of wood. Research in Nondestructive Evaluation, 1--18.Google Scholar
- Suckling, J., Parker, J., Dance, D., Astley, S., Hutt, I., Boggis, C., Taylor, P. 1994. The mammographic image analysis society digital mammogram database. In ExerptaMedica. International Congress Series, 1069(1994), 375--378.Google Scholar
- Gonzalez, R.C., WoodsR.E. 2009. Digital Image Processing. Nueva Jersey 2009. Google ScholarDigital Library
- Oldham, K. B., Spanier, J. 1974. The Fractional Calculus. New York: Academic, 1974.Google Scholar
- Podulubny, 1998. Fractional Differential Equation, Mathematics in Science & Engineering, 1998, Academic Press: California, USA.Google Scholar
- Pal, N. R., Bezdek, J. C. 1995. On cluster validity for the fuzzy c-means model. IEEE Transactions on Fuzzy systems, 3(1995), 370--379. Google ScholarDigital Library
- Ojala, T., Pietikainen, M., Maenpaa, T. 2002. Multiresolution grayscale and rotation invariant texture classification with local binary patterns. IEEE Transactions on pattern analysis and machine intelligence, 24(2002), 971--987. Google ScholarDigital Library
- Mehta, R., Egiazarian, K. 2016, Dominant rotated local binary patterns (DRLBP) for texture classification. Pattern Recognition Letters, 71(2016), 16--22. Google ScholarDigital Library
- Cortes, C., Vapnik, V. 1995. Support-vector networks. Machine learning, 20(1995), 273--297. Google ScholarDigital Library
Index Terms
- Breast Tissue Density Classification in Mammograms Based on Supervised Machine Learning Technique
Recommendations
A review of breast tissue classification in mammograms
RACS '11: Proceedings of the 2011 ACM Symposium on Research in Applied ComputationFor women in the U.S. breast cancer is the most commonly diagnosed cancer besides skin cancer and has become one of the major health issues in recent decades. Early detection through screening is one of key factors to reduce the death rates. The strong ...
Automated assessment of breast tissue density in digital mammograms
Mammographic density is known to be an important indicator of breast cancer risk. Classification of mammographic density based on statistical features has been investigated previously. However, in those approaches the entire breast including the ...
Detection of masses in mammograms with adaption to breast density using genetic algorithm, phylogenetic trees, LBP and SVM
Segmentation of the breast separates the skin and the background of the image is kept, with a good performance.High performance at the detection of the density of the breast.Efficient texture description method, based on the combination of Phylogenetic ...
Comments