Automatic Detection of Microcalcifications in ROI Images Based on PFCM and ANN

REFERENCES

• Ahmed, M. N., & Farag, A. A. (1997). Two-stage neural network for volume segmentation of medical images. Pattern Recognition Letters, 18, 1143–1151.
   Google Scholar
• Andina, D., & Pham, D. (2007). Computational intelligence for engineering and manufacturing (1st ed.). Springer Verlag.
   Google Scholar
• Balafar, M. A., Ramli, A. R., Saripan, M. I., Mahmud, R., Mashohor, S., & Balafar, M. (2008). New multi-scale medical image segmentation based on fuzzy c-mean (FCM). IEEE Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, 2008 (CITISIA 2008), Cyberjaya, pp. 66–70.
   Google Scholar
• Barrón-Adame, J. M., Cortina-Januchs, M. G., Vega-Corona, A., & Andina, D. (2012). Unsupervised system to classify SO2 pollutant concentrations in Salamanca, Mexico. Expert Systems with Applications, 39, 107–116.
   Google Scholar
• Basheer, I., & Hajmeer, M. (2000). Artificial neural networks: Fundamentals, computing, design, and application. Journal of Microbiological Methods, 43, 3–31.
   PubMed Web of Science ®Google Scholar
• Bezdek, J. (1981). Pattern recognition with fuzzy objective function algorithms. New York, NY: Plenum Press.
   Google Scholar
• Cheng, H., Cai, X., Chen, X., Hu, L., & Lou, X. (2003). Computer-aided detection and classification of microcalcifications in mammograms: A survey. Pattern Recognition, 36, 2967–2991.
   Web of Science ®Google Scholar
• Cheng, H., Shan, J., Ju, W., Guo, Y., & Zhang, L. (2010). Automated breast cancer detection and classification using ultrasound images: A survey. Pattern Recognition, 43, 299–317.
   Google Scholar
• Cheng, H., Wang, J., & Shi, X. (2004). Microcalcification detection using fuzzy logic and scale space approaches. Pattern Recognition, 37, 363–375.
   Google Scholar
• Cheng, H. D., Shi, X. J., Min, R., Hu, L. M., Cai, X. P., & Du, H. N. (2006). Approaches for automated detection and classification of masses in mammograms. Pattern Recognition, 39, 646–668.
   Web of Science ®Google Scholar
• Cortina-Januchs, M. G., Quintanilla-Dominguez, J., Vega-Corona, A. M., Tarquis, A., & Andina, D. (2011). Detection of pore space in CT soil images using artificial neural networks. Biogeosciences, 8, 8279–8288.
   Google Scholar
• Fu, J., Lee, S., Wong, S., Yeh, J., Wang, A., & Wu, H. (2005). Image segmentation feature selection and pattern classification for mammographic microcalcifications. Computerized Medical Imaging and Graphics, 29, 419–429.
   PubMed Web of Science ®Google Scholar
• Hung, W. L., Yang, M. S., & Chen, H. D. (2006). Parameter selection for suppressed fuzzy c-means with an application to MRI segmentation. Pattern Recognition Letters, 27, 424–438.
   Web of Science ®Google Scholar
• Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: A review. ACM Computing Surveys, 31, 264–323.
   Web of Science ®Google Scholar
• Jevtic, A., Quintanilla-Domínguez, J., Barrón-Adame, J. M., & Andina, D. (2011). Image segmentation using ant system-based clustering algorithm. Soft computing models in industrial and environmental applications, 6th International Conference SOCO 2011 (Vol. 87, pp. 35–45). Springer: Advances in Intelligent and Soft Computing.
   Google Scholar
• Krishnapuram, R., & Keller, J. M. (1993). A possibilistic approach to clustering. IEEE Transactions on Fuzzy Systems, 1, 98–110.
   Google Scholar
• Krishnapuram, R., & Keller, J. M. (1996). The possibilistic c-means algorithm: Insights and recommendations. IEEE Transactions on Fuzzy Systems, 4, 385–393.
   Web of Science ®Google Scholar
• Liu, B., Cheng, H., Huang, J., Tian, J., Tang, X., & Liu, J. (2010). Fully automatic and segmentation-robust classification of breast tumors based on local texture analysis of ultrasound images. Pattern Recognition, 43, 280–298.
   Google Scholar
• Marcano-Cedeño, A., Quintanilla-Domínguez, J., & Andina, D. (2011). Breast cancer classification applying artificial metaplasticity algorithm. Neurocomputing, 74, 1243–1250.
   Google Scholar
• Ojeda-Magaña, B., Quintanilla-Domínguez, J., Ruelas, R., & Andina, D. (2009). Images sub-segmentation with the PFCM clustering algorithm. 7th IEEE International Conference on Industrial Informatics, 2009 (INDIN 2009), Cardiff, pp. 499–503.
   Google Scholar
• Pal, N. R., & Pal, S. K. (1993). A review on image segmentation techniques. Pattern Recognition, 26, 1277–1294.
   Web of Science ®Google Scholar
• Pal, N., Pal, K., Keller, J., & Bezdek, J. (2005). A possibilistic fuzzy c-means clustering algorithm. IEEE Transactions on Fuzzy Systems, 13, 517–530.
   Web of Science ®Google Scholar
• Papadopoulos, A., Fotiadis, D., & Likas, A. (2002). An automatic microcalcification detection system based on a hybrid neural network classifier. Artificial Intelligence in Medicine, 25, 149–167.
   PubMed Web of Science ®Google Scholar
• Papadopoulos, A., Fotiadis, D., & Costaridou, L. (2008). Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques. Computers in Biology and Medicine, 38, 1045–1055.
   Google Scholar
• Qian, W., Mao, F., Sun, X., Zhang, Y., Song, D., & Clarke, R. A. (2002). An improved method of region grouping for microcalcification detection in digital mammograms. Computerized Medical Imaging and Graphics, 26, 361–368.
   Google Scholar
• Quintanilla-Domínguez, J., Cortina-Januchs, M. G., Ojeda-Magaña, B., Jevtic, A., Vega-Corona, A., & Andina, D. (2010). Microcalcification detection applying artificial neural networks and mathematical morphology in digital mammograms. Proceeding of World Automation Congress (WAC 2010), Kobe, pp. 1–6.
   Google Scholar
• Quintanilla-Dominguez, J., Ojeda-Magaña, B., Cortina-Januchs, M. G., Ruelas, R., Vega-Corona, A., & Andina, D. (2011). Image segmentation by fuzzy and possibilistic clustering algorithms for the identification of microcalcifications. Scientia Iranica, 18, 580–589.
   Google Scholar
• Rojas-Domínguez, A., & Nandi, A. K. (2009). Toward breast cancer diagnosis based on automated segmentation of masses in mammograms. Pattern Recognition, 42, 1138–1148.
   Google Scholar
• Stojic, T., & Reljin, B. (2010). Enhancement of microcalcifications in digitized mammograms: Multifractal and mathematical morphology approach. FME Transactions, 38, 1–9.
   Google Scholar
• Suckling, J., Parker, J., & Dance, D. (1994). The mammographic image analysis society digital mammogram database. Exerpta Medica International Congress Series, 1069, 375–378.
   Google Scholar
• Vega, A., Quintanilla-Domínguez, J., Ojeda-Magaña, B., Cortina-Januchs, M. G., Marcano-Cedeño, A., Ruelas, R., & Andina, D. (2011). Microcalcifications detection using PFCM and ANN. 3rd Mexican conference on pattern recognition (p. 1). Berlin, Heidelberg: Springer.
   Google Scholar
• Wei, L., Yang, Y., & Nishikawa, R. M. (2009). Microcalcification classification assisted by content-based image retrieval for breast cancer diagnosis. Pattern Recognition, 42, 1126–1132.
   PubMed Web of Science ®Google Scholar
• Wirth, M., Fraschini, M., & Lyon, J. (2004). Contrast enhancement of microcalcifications in mammograms using morphological enhancement and non-flat structuring elements. Proceedings. 17th IEEE Symposium on Computer-Based Medical Systems, 2004 (CBMS 2004), Bethesda, pp. 134–139.
   Google Scholar
• Xia, Y., Feng, (. D, Wang, T., Zhao, R., & Zhang, Y. (2007). Image segmentation by clustering of spatial patterns. Pattern Recognition Letters, 28, 1548–1555.
   Web of Science ®Google Scholar
• Xue, J. H., Ruan, S., Moretti, B., Revenu, M., & Bloyet, D. (2001). Knowledge-based segmentation and labeling of brain structures from MRI images. Pattern Recognition Letters, 22, 395–405.
   Web of Science ®Google Scholar
• Xue, J. H., Pizurica, A., Philips, W., Kerre, E., Van De Walle, R., & Lemahieu, I. (2003). An integrated method of adaptive enhancement for unsupervised segmentation of MRI brain images. Pattern Recognition Letters, 24, 2549–2560.
   Google Scholar