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Image Based Classification Platform: Application to Breast Cancer Diagnosis

Image Based Classification Platform: Application to Breast Cancer Diagnosis

Paolo J. S. Gonçalves (Polytechnic Institute of Castelo Branco, Portugal & Technical University of Lisbon, Portugal), Rui J. Almeida (Erasmus University Rotterdam, The Netherlands), João R. Caldas Pinto (Technical University of Lisbon, Portugal), Susana M. Vieira (Technical University of Lisbon, Portugal) and João M. C. Sousa (Technical University of Lisbon, Portugal)
ISBN13: 9781466639904|ISBN10: 1466639903|EISBN13: 9781466639911
DOI: 10.4018/978-1-4666-3990-4.ch031
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MLA

Gonçalves, Paolo J. S., Rui J. Almeida, João R. Caldas Pinto, Susana M. Vieira and João M. C. Sousa. "Image Based Classification Platform: Application to Breast Cancer Diagnosis." Handbook of Research on ICTs and Management Systems for Improving Efficiency in Healthcare and Social Care. IGI Global, 2013. 595-613. Web. 27 Mar. 2020. doi:10.4018/978-1-4666-3990-4.ch031

APA

Gonçalves, P. J., Almeida, R. J., Pinto, J. R., Vieira, S. M., & Sousa, J. M. (2013). Image Based Classification Platform: Application to Breast Cancer Diagnosis. In M. Cruz-Cunha, I. Miranda, & P. Gonçalves (Eds.), Handbook of Research on ICTs and Management Systems for Improving Efficiency in Healthcare and Social Care (pp. 595-613). Hershey, PA: IGI Global. doi:10.4018/978-1-4666-3990-4.ch031

Chicago

Gonçalves, Paolo J. S., Rui J. Almeida, João R. Caldas Pinto, Susana M. Vieira and João M. C. Sousa. "Image Based Classification Platform: Application to Breast Cancer Diagnosis." In Handbook of Research on ICTs and Management Systems for Improving Efficiency in Healthcare and Social Care, ed. Maria Manuela Cruz-Cunha, Isabel Maria Miranda and Patricia Gonçalves, 595-613 (2013), accessed March 27, 2020. doi:10.4018/978-1-4666-3990-4.ch031

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Abstract

The high number of exams that is done in healthcare institutions increases the medical doctors’ workload, leading to poor working conditions and the increase of wrong diagnoses. As consequence, an automatic system that can help medical doctors in diagnostic tasks is of major interest to any healthcare institution. The chapter proposes an Image Based Classification Platform suitable to help Medical Doctors diagnosing breast cancer, based on mammograms, i.e., to detect if a tumor is present in the image. The platform is twofold, i.e., in the first part the image descriptors are extracted from the image using image-processing algorithms. The obtained descriptors are used in the second part. The second part is related to classification, where computational intelligence methods are used to classify a given image, based on the descriptors obtained in the first phase. Texture analysis based on co-occurrence matrices are applied to obtain the descriptors from the MIAS database of mammograms. From these descriptors, fuzzy models, neural networks, and support vector machines are successfully used to classify the mammograms and obtain a diagnosis.

References

Alexandre, H., & Caldas Pinto, J. (2006). New wavelets based features for natural surface indexing. In Proceedings of the First International Conference on Computer Vision Theory and Applications, (vol. 1, pp. 311-316). Setúbal, Portugal: IEEE.
Almeida, R. J., & Sousa, J. M. C. (2006). Comparison of fuzzy clustering algorithms for classification. In Proceedings of International Symposium on Evolving Fuzzy Systems (pp. 112–117). Lake District, UK: IEEE.
Bankman I. N. (2000). Handbook of medical imaging processing and analysis. New York: Academic Press.
Berger J. (1993). Statistical decision theory and Bayesian analysis. New York: Springer.
Bezdek J. C. (1981). Pattern recognition with fuzzy objective function algorithms. New York: Plenum Press. 10.1007/978-1-4757-0450-1
Bozek J. Mustra M. Delac K. Grgic M. (2009). A survey of image processing algorithms in digital mammography.[). Berlin: Springer.]. Proceedings of Recent Advances in Multimedia Signal Processing and Communications, 231, 631–657. 10.1007/978-3-642-02900-4_24
Cady B. Chung M. (2005). Mammographic screening: no longer controversial.American Journal of Clinical Oncology, 28(1), 1–4. 10.1097/01.coc.0000150720.15450.0515685026
Canu, S., Grandvalet, Y., Guigue, V., & Rakotomamonjy, A. (2005). Svm and kernel methods matlab toolbox: Perception systmes et information. INSA de Rouen. Retrieved November 14, 2011 from http://asi.insa-rouen.fr/enseignants/~arakotom/toolbox/index.html
Castilho H. P. Goncalves P. J. S. Pinto J. R. C. Serafim A. L. (2007). Intelligent real-time fabric defect detection.Lecture Notes in Computer Science, 4633, 1297–1307. 10.1007/978-3-540-74260-9_115
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(4), 646–668. 10.1016/j.patcog.2005.07.006
Chiu S. (1994). Fuzzy model identification based on cluster estimation.Journal of Intelligent and Fuzzy Systems, 2(3), 267–278.
Chiu S. (1996). Selecting input variables for fuzzy models.Journal of Intelligent and Fuzzy Systems, 4(4), 243–256.
Cohen J. (1960). A coefficient of agreement for nominal scales.Educational and Psychological Measurement, 20(1), 37–46. 10.1177/001316446002000104
Ferlay, J. (2010). GLOBOCAN 2008, cancer incidence and mortality worldwide. Retrieved November 14, 2011 from http://globocan.iarc.fr
Fernandes F. C. Brasil L. M. Lamas J. M. Guadagnin R. (2010). Breast cancer image assessment using an adaptative network-based fuzzy inference system.Pattern Recognition and Image Analysis, 20(2), 192–200. 10.1134/S1054661810020112
Gustfason, D., & Kessel, W. (1979). Fuzzy clustering with a fuzzy covariance matrix. In Proceedings of IEEE Conference on Decision and Control (pp. 761–766). San Diego, CA: IEEE.
Haralick R. M. Shanmugan K. Dinstein I. (1973). Textural features for image classification.IEEE Transactions on Systems, Man, and Cybernetics, 3(6), 610–621. 10.1109/TSMC.1973.4309314
Heath, M., Bowyer, K., Kopans, D., Moore, R., & Kegelmeyer, W. P. (2001). The digital database for screening mammography. in M.J. Yaffe (Ed.), Proceedings of the Fifth International Workshop on Digital Mammography (pp. 212-218). Medical Physics Publishing.
Hughes J. K. Michtom G. C. Michtom J. I. (1987). A structured approach to programming (2nd ed.). Upper Saddle River, NJ: Prentice-Hall, Inc.
Jang J. S. R. (1993). Anfis: Adaptive-network-based fuzzy inference system.IEEE Transactions on Systems, Man, and Cybernetics, 23, 665–684. 10.1109/21.256541
Keller J. M. Pal N. R. Pal K. Bezdek J. C. (2005). A possibilistic fuzzy c-means clustering algorithm.IEEE Transactions on Fuzzy Systems, 13(4), 517–530. 10.1109/TFUZZ.2004.840099
Kohavi R. John G. H. (1997). Wrappers for feature subset selection.Artificial Intelligence, 97(1-2), 273–324. 10.1016/S0004-3702(97)00043-X
Kohavi R. Provost F. (1998). Glossary of terms: Editorial for the special issue on applications of machine learning and the knowledge discovery process.Machine Learning, 30(2/3).
Krishnapuram R. Keller J. M. (1993). A possibilistic approach to clustering.IEEE Transactions on Fuzzy Systems, 1(2), 98–110. 10.1109/91.227387
Kubat M. Holte R. Matwin S. (1998). Machine learning for the detection of oil spills in satellite radar images.Machine Learning, 30, 195–215. 10.1023/A:1007452223027
Lewis, D., & Gale, W. (1994). A sequential algorithm for training text classifiers. In Proceedings of ACM-SIGIR (pp. 3-12). Dublin, Ireland: ACM Press.
Lightstone M. Mitra S. K. (1997). Quadtree optimization for image and video coding.Journal of VLSI Signal Processing Systems, 17(2-3), 215–224.
Mamdani E. H. (1977). Application of fuzzy logic to approximate reasoning using linguistic systems.IEEE Transactions on Computers, 26(12), 1182–1191. 10.1109/TC.1977.1674779
Maynard J. (1972). Modular programming. New York: Petrocelli Books.
Mohanty A. K. Champati P. K. Swain S. K. Lenka S. K. (2011). A review on computer aided mammography for breast cancer diagnosis and classification using image mining methodology.International Journal of Computer Science and Communication, 2(2), 531–538.
Organisation for Economic Co-Operation and Development – OECD . (2010). Health at a glance: Europe 2010. Paris: OECD Publishing.
Pal, N. R., Pal, K., & Bezdek, J. C. (1997). A mixed c-means clustering model. In Proceedings of the Sixth IEEE International Conference on Fuzzy Systems, (vol. 1, pp. 11–21). Barcelona: IEEE.
Ramalho, M., Caldas Pinto, J., & Marcolino, A. (2000). Clustering algorithm for colour segmentation. In Proceedings of the V Ibero-American Symposium on Pattern Recognition, (vol. 1, pp. 611-617). Lisbon, Portugal: IASPR.
Rangayyan R. M. Ayres F. J. Desautels J. E. (2007). A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs.Journal of the Franklin Institute, 344, 312–348. 10.1016/j.jfranklin.2006.09.003
Rangayyan R. M. Banik S. Desautels J. E. L. (2010). Computer-aided detection of architectural distortion in prior mammograms of interval cancer.Journal of Digital Imaging, 23(5), 611–631. 10.1007/s10278-009-9257-x20127270
Sampaio W. B. Diniz E. M. Silva A. C. de Paiva A. C. Gattass M. (2011). Detection of masses in mammogram images using CNN, geostatistic functions and SVM.Computers in Biology and Medicine, 41(8), 653–664. 10.1016/j.compbiomed.2011.05.01721703605
Sousa J. M. C. Gil J. M. Pinto J. R. C. (2007). Word indexing of ancient documents using fuzzy classification.IEEE Transactions on Fuzzy Systems, 15(5), 852–862. 10.1109/TFUZZ.2006.889933
Strobach P. (1991). Quadtree-structured recursive plane decomposition coding of images.IEEE Transactions on Signal Processing, 39(1), 1380–1397. 10.1109/78.136544
Suckling J. (1994). The mammographic image analysis society digital mammogram database.Exerpta Medica, 1069, 375–378.
Sugeno M. Yasukawa T. (1993). A fuzzy-logic-based approach to qualitative modeling.IEEE Transactions on Fuzzy Systems, 1(1), 7–31. 10.1109/TFUZZ.1993.390281
Tahmasbi A. Saki F. Shokouhi S. B. (2011). Classification of benign and malignant masses based on Zernike moments.Computers in Biology and Medicine, 41(8), 726–735. 10.1016/j.compbiomed.2011.06.00921722886
Takagi T. Sugeno M. (1985). Fuzzy identification of systems and its application to modeling and control.IEEE Transactions on Systems, Man, and Cybernetics, 15(1), 116–132. 10.1109/TSMC.1985.6313399
Tang J. Rangayyan R. M. Xu J. Naqa I. L. Yang Y. (2009). Computer-aided detection and diagnosis of breast cancer with mammography: Recent advances.IEEE Transactions on Information Technology in Biomedicine, 13(2), 236–251. 10.1109/TITB.2008.200944119171527
Vaisey, D. J., & Gersho, A. (1987). Variable block-size image coding. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, (vol. 12, pp. 1051–1054). Dallas, TX: IEEE.
Vaisey D. J. Gersho A. (1992). Image compression with variable block size segmentation.IEEE Transactions on Signal Processing, 40(1), 2040–2060. 10.1109/78.150005
Vapnik, V. N. (1998). Statistical learning theory. New York: A Wiley-Interscience Publication.
Yu Y. Cheng Q. (2003). Mrf parameter estimation by an accelerated method.Pattern Recognition Letters, 24, 1251–1259. 10.1016/S0167-8655(02)00319-7
Zadeh L. A. (1973). Outline of a new approach to the analysis of complex systems and decision processes.IEEE Transactions on Systems, Man, and Cybernetics, 3(1), 28–44. 10.1109/TSMC.1973.5408575
Zhang G. P. (2000). Neural networks for classification: A survey.IEEE Transactions on Systems, Man and Cybernetics. Part C, Applications and Reviews, 30(4), 451–462. 10.1109/5326.897072

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