Abstract
Screening programs use mammography as primary diagnostic tool for detecting breast cancer at an early stage. The diagnosis of some lesions, such as microcalcifications, is still difficult today for radiologists. In this paper, we proposed an automatic model for characterizing and discriminating tissue in normal/abnormal and benign/malign in digital mammograms, as support tool for the radiologists. We trained a Random Forest classifier on some textural features extracted on a multiscale image decomposition based on the Haar wavelet transform combined with the interest points and corners detected by using Speeded Up Robust Feature (SURF) and Minimum Eigenvalue Algorithm (MinEigenAlg), respectively. We tested the proposed model on 192 ROIs extracted from 176 digital mammograms of a public database. The model proposed was high performing in the prediction of the normal/abnormal and benign/malignant ROIs, with a median AUC value of \(98.46\%\) and \(94.19\%\), respectively. The experimental result was comparable with related work performance.
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References
Elter, M., Horsch, A.: CADx of mammographic masses and clustered microcalcifications: a review. Med. Phys. 36(6), 2052–2068 (2009)
Howell, A.: The emerging breast cancer epidemic: early diagnosis and treatment. Breast Cancer Res. 12(4), S10 (2010)
Breast Cancer Facts. http://www.uthscsa.edu/hscnews/pdf/. Accessed Apr 2010
Fletcher, S.W., Elmore, J.G.: Mammographic screening for breast cancer. N. Engl. J. Med. 348(17), 1672–1680 (2003)
Elmore, J.G., Armstrong, K., Lehman, C.D., Fletcher, S.W.: Screening for breast cancer. JAMA 293(10), 1245–1256 (2006)
Cheng, H.D., Cai, X., Chen, X., Hu, L., Lou, X.: Computer-aided detection and classification of microcalcifications in mammograms: a survey. Pattern Recogn. 36(12), 2967–2991 (2003)
Brown, J., Bryan, S., Warren, R.: Mammography screening: an incremental cost effectiveness analysis of double versus single reading of mammograms. BMJ 312(7034), 809–812 (1996)
McCormack, V.A., dos Santos Silva, I.: Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol. Prev. Biomark. 15(6), 1159–1169 (2006)
Wolfe, J.N.: Breast patterns as an index of risk for developing breast cancer. Am. J. Roentgenol. 126(6), 1130–1137 (1976)
Jiang, Y., Nishikawa, R.M., Wolverton, D.E., Metz, C.E., Giger, M.L., Schmidt, R.A., Vyborny, C.J., Doi, K.: Malignant and benign clustered microcalcifications: automated feature analysis and classification. Radiology 198(3), 671–678 (1996)
Chan, H.P., Sahiner, B., Lam, K.L., Petrick, N., Helvie, M.A., Goodsitt, M.M., Adler, D.D.: Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces. Med. Phys. 25(10), 2007–2019 (1998)
Nakayama, R., Uchiyama, Y., Watanabe, R., Katsuragawa, S., Namba, K.: Computer-aided diagnosis scheme for histological classification of clustered microcalcifications on magnification mammograms. Med. Phys. 31(4), 789–799 (2004)
Sampat, M.P., Markey, M.K., Bovik, A.C.: Computer-aided detection and diagnosis in mammography. Handb. Image Video Process. 2(1), 1195–1217 (2005)
Zhang, X., Homma, N., Goto, S., Kawasumi, Y., Ishibashi, T., Abe, M., Sugita, N., Yoshizawa, M.: A hybrid image filtering method for computer-aided detection of microcalcification clusters in mammograms. J. Med. Eng. 2013, 8 p. (2013). Article no. 615254
Vivona, L., Cascio, D., Fauci, F., Raso, G.: Fuzzy technique for microcalcifications clustering in digital mammograms. BMC Med. Imaging 14(1), 23 (2014)
Wang, J., Nishikawa, R.M., Yang, Y.: Improving the accuracy in detection of clustered microcalcifications with a context-sensitive classification model. Med. Phys. 43(1), 159–170 (2016)
Oliver, A., Torrent, A., Lladó, X., Tortajada, M., Tortajada, L., Sentís, M., Freixenet, J., Zwiggelaar, R.: Automatic microcalcification and cluster detection for digital and digitised mammograms. Knowl.-Based Syst. 28, 68–75 (2012)
Gallardo-Caballero, R., García-Orellana, C.J., García-Manso, A., González-Velasco, H.M., Macías-Macías, M.: Independent component analysis to detect clustered microcalcification breast cancers. Sci. World J. 2012, 6 p. (2012). Article no. 540457
Jian, W., Sun, X., Luo, S.: Computer-aided diagnosis of breast microcalcifications based on dual-tree complex wavelet transform. Biomed. Eng. Online 11(1), 96 (2012)
Phadke, A.C., Rege, P.P.: Detection and classification of microcalcifications using discrete wavelet transform. Int. J. Emerg. Trends Technol. Comput. Sci. 2(4), 130–134 (2013)
Khehra, B.S., Pharwaha, A.P.S.: Classification of clustered microcalcifications using MLFFBP-ANN and SVM. Egypt. Inform. J. 17(1), 11–20 (2016)
Boulehmi, H., Mahersia, H., Hamrouni, K.: A new CAD system for breast microcalcifications diagnosis. Int. J. Adv. Comput. Sci. Appl. 7(4), 133–143 (2016)
Chen, Z., Strange, H., Oliver, A., Denton, E.R., Boggis, C., Zwiggelaar, R.: Topological modeling and classification of mammographic microcalcification clusters. IEEE Trans. Biomed. Eng. 62(4), 1203–1214 (2015)
Fanizzi, A., Basile, T.M.A., Losurdo, L., Amoroso, N., Bellotti, R., Bottigli, U., Dentamaro, R., Didonna, V., Fausto, A., Massafra, R., Moschetta, M., Tamborra, P., Tangaro, S., La Forgia, D.: Hough transform for clustered microcalcifications detection in full-field digital mammograms. In: Applications of Digital Image Processing XL, vol. 10396, p. 1039616. International Society for Optics and Photonics, San Diego (2017)
Sklansky, J.: On the Hough technique for curve detection. IEEE Trans. Comput. C–27(10), 923–926 (1978)
Pedersen, S.J.K.: Circular hough transform. Aalborg Univ. Vis. Graph. Interact. Syst. 123, 123 (2007)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall, Upper Saddle River (2006)
Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989)
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)
Shi, J., Tomasi, C.: Good features to track. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Proceedings CVPR 1994, pp. 593–600. IEEE (1994)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Ramos-Pollán, R., Guevara-López, M.A., Suárez-Ortega, C., Díaz-Herrero, G., Franco-Valiente, J.M., Rubio-del-Solar, M., González-de-Posada, N., Pires Vaz, M.A., Loureiro, J., Ramos, I.: Discovering mammography-based machine learning classifiers for breast cancer diagnosis. J. Med. Syst. 36(4), 2259–2269 (2012)
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This work was supported by founding from Italian Ministry of Health “Ricerca Corrente 2016”.
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Losurdo, L. et al. (2018). A Combined Approach of Multiscale Texture Analysis and Interest Point/Corner Detectors for Microcalcifications Diagnosis. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2018. Lecture Notes in Computer Science(), vol 10813. Springer, Cham. https://doi.org/10.1007/978-3-319-78723-7_26
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DOI: https://doi.org/10.1007/978-3-319-78723-7_26
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