Abstract
Breast cancer remains a leading cause of death among women worldwide. Mammography is one of the non-invasive methods to find breast tumors, which is very useful in the detection of cancer. Microcalcifications are one of the anomalies of this disease, and these appear as small white spots on the images. Several computer-aided systems (CAD) have been developed for the detection of anomalies related to the disease. However, one of the critical parts is the segmentation process, as the rate of detection of anomalies in the breast by mammography largely depends on this process. In addition, a low detection endangers women’s lives, while a high detection of suspicious elements have excessive cost. Hence, in this work we do a comparative study of segmentation algorithms, specifically three of them derived from the family of c-Means, and we use the NU (Non-Uniformity) measure as a quality indicator of segmentation results. For the study we use 10 images of the MIAS database, and the algorithms are applied to the regions of interest (ROI). Results are interesting, the novel method of sub-segmentation allows continuous and gradual adjustment, which is better adapted to the regions of micro calcification, and this results in smaller NU values. The NU measure can be used as an indication of quality, which depends on the number of pixels and the homogeneity of the segmented regions, although it should be put in the context of the application to avoid making misinterpretations.
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References
Pham, D.L., Xu, C., Prince, J.L.: A Survey of Current Methods in Medical Image Segmentation. Annual Review of Biomedical Engineering 2(4), 315–338 (2000)
Sampat, P.M., Markey, M.K., Bovik, A.C.: Computer-aided detection and diagnosis in mammography. In: Bovik, A.C. (ed.) Handbook of Image and Video Processing, 2nd edn., pp. 1195–1217. Academic Press, New York (2005)
Lee, N., Laine, A.F., Marquez, G., Levsky, J.M., Gohagan, J.K.: Potential of computer-aided diagnosis to improve CT lung cancer screening. IEEE Rev. Biomed. Eng. 2, 136–146 (2009)
Hernandez-Cisnero, R.R., Terashima-Marn, H.: Evolutionary neural networks applied to the classification of microcalcification clusters in digital mammograms. Proc. IEEE Congr. Evol. Comput., 2459–2466 (2006)
Tang, J., Rangayyan, R.M., Xu, J., El Naga, I., Yang, Y.: Computeraided detection and diagnosis of breast cancer with mammography: Recent advances. IEEE Trans. Inf. Technol. Biomed. 13(2), 236–251 (2009)
Bocciglione, G., Chainese, A., Picariello, A.: Computer aided detection of microcalcifications in digital mammograms. Comput. Biol. Med. 30(5), 267–286 (2009)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Publishing House of Electronics Industry, Beijing (2007)
Zhang, Y.J.: A survey on evaluation methods for image segmentation. Pattern Recogn. 29(8), 1335–1346 (1996)
MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proc. 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297. University of California Press, Berkeley (1967)
Bezdek, J.C., Keller, J., Krishnapuram, R., Pal, N.R.: Fuzzy models and algorithms for pattern recognition and image processing, Boston, London (1999)
Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs (1998)
Runkler, T.A.: Ant colony optimization of clustering models. Int. J. Intell. Syst. 20(12), 1233–1251 (2005)
Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J. Cybernetics 3(3), 32–57 (1973)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers, Norwell (1981)
Pal, N.R., Pal, K., Keller, J.M., Bezdek, J.C.: A possibilitic fuzzy c-means clustering algorithm. IEEE T. Fuzzy Syst. 13(4), 517–530 (2005)
Dengler, J., Behrens, S., Desega, J.F.: Segmentation of Microcalcifications in Mammograms. IEEE T. Med. Imaging 12(4), 634–642 (1993)
Serra, J.: Images analysis and mathematical morphological. Academic Press, New York (1982)
Pal, N., Pal, S.: A review on image segmentation techniques. IEEE T. Fuzzy Syst. 13(4), 517–530 (1993)
Fu, J., Lee, S., Wong, S., Yeh, J., Wang, A., Wu, H.: Image segmentation feature selection and pattern classification for mammographic microcalcifications. Med. Imag. Grap. 29(6), 419–429 (2005)
Ojeda-Magaña, B., Quintanilla-Domínguez, J., Ruelas, R., Andina, D.: Images sub-segmentation with the PFCM clustering algorithm. In: Proc. 7th IEEE Int. Conf. Industrial Informatics, pp. 499–503 (2009)
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Guardado-Medina, R.O., Ojeda-Magaña, B., Quintanilla-Domínguez, J., Ruelas, R., Andina, D. (2014). Quality of Microcalcification Segmentation in Mammograms by Clustering Algorithms. In: Herrero, Á., et al. International Joint Conference SOCO’13-CISIS’13-ICEUTE’13. Advances in Intelligent Systems and Computing, vol 239. Springer, Cham. https://doi.org/10.1007/978-3-319-01854-6_31
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DOI: https://doi.org/10.1007/978-3-319-01854-6_31
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