Abstract
Early automatic breast cancer detection from mammograms is based on the extraction of lesions, known as microcalcifications (MCs). This paper proposes a new and simple system for microcalcification detection to assist in early breast cancer detection. This work uses the two most recognized public mammogram databases, MIAS and DDSM. We are introducing a MC detection method based on (1) Beucher gradient for detection of regions of interest (ROIs), (2) an annulus model for extraction of few and effective features from candidates to MCs, and (3) one classification stage with two different classifiers, k Nearest Neighbor (KNN) and Support Vector Machine (SVM). For dense mammograms in the MIAS database, the performance metrics achieved are sensitivity of 0.9835, false alarm rate of 0.0083, accuracy of 0.9835, and area under the ROC curve of 0.9980 with a KNN classifier. The proposed MC detection method, based on a KNN classifier, achieves, a sensitivity, false positive rate, accuracy and area under the ROC curve of 0.9813, 0.0224, 0.9795 and 0.9974 for the MIAS database; and 0.9035, 0.0439, 0.9298 and 0.9759 for the DDSM database. By slightly reducing the true positive rate the method achieves three instances with false positive rate of 0: 2 on fatty mammograms with KNN and SVM, and one on dense with SVM. The proposed method gives better results than those from state of the art literature, when the mammograms are classified in fatty, fatty-glandular, and dense.
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WHO. WHO | Breast cancer. 2017. Who. Retrieved December 18, 2017, from http://www.who.int/cancer/prevention/diagnosis-screening/breast-cancer/en/.
Coburn, N. G., Chung, M. A., Fulton, J., and Cady, B., Decreased breast Cancer tumor size, stage, and mortality in Rhode Island: An example of a well-screened population. Canc. Contrl. 11(4):222–230, 2004. https://doi.org/10.1177/107327480401100403.
Cho, K. R., Seo, B. K., Woo, O. H., Song, S. E., Choi, J., Whang, S. Y., … Chung, H. H. (2016). Breast Cancer detection in a screening population: Comparison of digital mammography, computer-aided detection applied to digital mammography and breast ultrasound. J. Breast Canc., 19(3), 316. doi:https://doi.org/10.4048/jbc.2016.19.3.316
Fischer, U., Baum, F., Obenauer, S., Luftner-Nagel, S., von Heyden, D., Vosshenrich, R., & Grabbe, E. (2002). Comparative study in patients with microcalcifications: Full-field digital mammography vs screen-film mammography. Eur. Radiol., 12(11), 2679–2683. doi:https://doi.org/10.1007/s00330-002-1354-x
Breastcancer.org. Diagnosis of DCIS. 2015. Retrieved December 18, 2017, from http://www.breastcancer.org/symptoms/types/dcis/diagnosis
Pak, F., Kanan, H. R., and Alikhassi, A., Breast cancer detection and classification in digital mammography based on non-subsampled Contourlet transform (NSCT) and super resolution. Comput. Methods Prog. Biomed. 122(2):89–107, 2015. https://doi.org/10.1016/j.cmpb.2015.06.009.
Institute, N. C. Mammograms. 2016. Retrieved August 8, 2017, from https://www.cancer.gov/types/breast/mammograms-fact-sheet#q1.
Dheeba, J., and Selvi, S. T., A swarm optimized neural network system for classification of microcalcification in mammograms. J. Med. Syst. 36(5):3051–3061, 2012. https://doi.org/10.1007/s10916-011-9781-3.
Linguraru, M. G., Marias, K., English, R., and Brady, M., A biologically inspired algorithm for microcalcification cluster detection. Med. Image Anal. 10(6):850–862, 2006. https://doi.org/10.1016/j.media.2006.07.004.
Duarte, M. A., Alvarenga, A. V., Azevedo, C. M., Calas, M. J. G., Infantosi, A. F. C., and Pereira, W. C. A., Evaluating geodesic active contours in microcalcifications segmentation on mammograms. Comput. Methods Prog. Biomed. 122(3):304–315, 2015. https://doi.org/10.1016/j.cmpb.2015.08.016.
Alasadi, A. H. H., and Al-Saedi, A. K. H., A method for microcalcifications detection in breast mammograms. J. Med. Syst. 41(4):1–9, 2017. https://doi.org/10.1007/s10916-017-0714-7.
Suckling, J., Parker, J., Dance, D., Astley, S., Hutt, I., Boggis, C., … Savage, J. (1994). The mammographic image analysis society digital mammogram database, Experta Medica Int. Cong. Ser., 1069(JANUARY 1994), 375–378.
Heath, M., Bowyer, K., Kopans, D., Moore, R., and Kegelmeyer, P., The digital database for screening mammography. Proc. Fifth Int. Workshop Digit. Mammograp., 212–218. doi:https://doi.org/10.1007/978-94-011-5318-8_75, 2001.
Guo, Y., Dong, M., Yang, Z., Gao, X., Wang, K., Luo, C. et al., A new method of detecting micro-calcification clusters in mammograms using contourlet transform and non-linking simplified PCNN. Comput. Methods Prog. Biomed. 130:31–45, 2016. https://doi.org/10.1016/j.cmpb.2016.02.019.
Vivona, L., Cascio, D., Fauci, F., and Raso, G., Fuzzy technique for microcalcifications clustering in digital mammograms. BMC Med. Imag. 14(1):23, 2014. https://doi.org/10.1186/1471-2342-14-23.
Mohamed, H., Mabrouk, M. S., and Sharawy, A., Computer aided detection system for micro calcifications in digital mammograms. Comput. Methods Prog. Biomed. 116(3):226–235, 2014. https://doi.org/10.1016/j.cmpb.2014.04.010.
Chen, Z., Strange, H., Oliver, A., Denton, E. R. E., Boggis, C., and Zwiggelaar, R., Topological modeling and classification of mammographic microcalcification clusters. IEEE Trans. Biomed. Eng. 62(4):1203–1214, 2015. https://doi.org/10.1109/TBME.2014.2385102.
Ashiru, O., and Zwiggelaar, R., Classification of mammographic microcalcification clusters using a combination of topological and location modelling. 2016 Sixth Int. Conf. Image Process. Theory, Tools Appl. (IPTA) (pp. 1–6). IEEE. 2016. doi:https://doi.org/10.1109/IPTA.2016.7820986.
Hu, K., Yang, W., and Gao, X., Microcalcification diagnosis in digital mammography using extreme learning machine based on hidden Markov tree model of dual-tree complex wavelet transform. Expert Syst. Appl. 86:135–144, 2017. https://doi.org/10.1016/j.eswa.2017.05.062.
AbuBaker, A. A., Automatic microcalcification detection using wavelet transform. Int. J. Comput. Theor. Eng. 7(1):40–45, 2014. https://doi.org/10.7763/IJCTE.2015.V7.927.
Muthuvel, M., Thangaraju, B., and Chinnasamy, G., Microcalcification cluster detection using multiscale products based hessian matrix via the Tsallis thresholding scheme. Pattern Recogn. Lett., 2017. https://doi.org/10.1016/j.patrec.2017.05.002.
Rivest, J. -F., Soille, P., and Beucher, S., Morphological gradients. In E. R. Dougherty, J. T. Astola, & C. G. Boncelet, Jr. (Eds.), (Vol. 1658, p. 139, 1992. doi:https://doi.org/10.1117/12.58373.
Acknowledgements
DDSM images are a courtesy of TM Desermo, Dept. of Medical Informatics, RWTH Aachen, Germany”. Jonathan Hernández-Capistran likes to thank to Council of Science and Technology (CONACYT) for doctoral scholarship with CVU No. 414681.
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Jonathan Hernández-Capistrán declares that he has no conflict of interest. Jorge F. Martínez-Carballido declares that he has no conflict of interest. Roberto Rosas-Romero declares that he has no conflict of interest.
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Hernández-Capistrán, J., Martínez-Carballido, J.F. & Rosas-Romero, R. False Positive Reduction by an Annular Model as a Set of Few Features for Microcalcification Detection to Assist Early Diagnosis of Breast Cancer. J Med Syst 42, 134 (2018). https://doi.org/10.1007/s10916-018-0989-3
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DOI: https://doi.org/10.1007/s10916-018-0989-3