Diagnóstico de Câncer de Mama em Mamografias baseado na Análise da Diversidade de Padrões Circulares
Abstract
This article aims to provide a better technique for analysis of texture in mammography images, to improve the diagnostic rate of breast cancer. The diagnosis made by a specialist depends on factors such as experience, quality of mammography and the patient’s own characteristics. The study seeks to use computational methods in order to provide a second opinion to the expert, using image representation techniques in the form of quantized circular patterns through co-occurrence matrices. After the representation, the texture is calculated through the diversity of analysis and classified using Support Vector Machines.
References
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Nanni, L., Lumini, A., and Brahnam, S. (2010). Local binary patterns variants as texture descriptors for medical image analysis. Artificial intelligence in medicine, 49(2):117– 125.
Ojala, T., Pietikainen, M., and Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(7):971–987.
Sun, C. and Wee, W. G. (1983). Neighboring gray level dependence matrix for texture classification. Computer Vision, Graphics, and Image Processing, 23(3):341–352.
Xinli, W., Albregtsen, F., and Foyn, B. (1995). Texture analysis using gray level gap length matrix. In Theory and Applications of Image Analysis II: Selected Papers from the 9th Scandinavian Conference on Image Analysis, volume 232, page 65. World Scientific.
Chang, C.-C. and Lin, C.-J. (2011). Libsvm: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3):27.
dos Santos, V. K. (2009). Uma generalização da distribuição do índice de diverisidade generalizado por good com aplicações em ciências agrárias. Master’s thesis, Universidade Federal Rural de Pernanbuco, Recife.
Galloway, M. M. (1975). Texture analysis using gray level run lengths. Computer graphics and image processing, 4(2):172–179.
Gonzalez, R. C. and Woods, R. E. (2008). Digital Image Processing. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 3nd edition.
Haralick, R. M., Shanmugam, K., and Dinstein, I. H. (1973). Textural features for image classification. Systems, Man and Cybernetics, IEEE Transactions on, (6):610–621.
Heath, M., Bowyer, K., Kopans, D., Moore, R., and Kegelmeyer, P. (2000). The digital database for screening mammography. In Proceedings of the 5th international workshop on digital mammography, pages 212–218. Citeseer.
Kinoshita, S., Pereira, R., Honda, M., Rodrigues, J., and Azevedo-Marques, P. (2004). An automatic method for detection of the nipple and pectoral muscle in digitized mammograms. In Congresso Latino-Americano de Engenharia Biomédica (CLAEB’2004).
Nanni, L., Lumini, A., and Brahnam, S. (2010). Local binary patterns variants as texture descriptors for medical image analysis. Artificial intelligence in medicine, 49(2):117– 125.
Ojala, T., Pietikainen, M., and Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(7):971–987.
Sun, C. and Wee, W. G. (1983). Neighboring gray level dependence matrix for texture classification. Computer Vision, Graphics, and Image Processing, 23(3):341–352.
Xinli, W., Albregtsen, F., and Foyn, B. (1995). Texture analysis using gray level gap length matrix. In Theory and Applications of Image Analysis II: Selected Papers from the 9th Scandinavian Conference on Image Analysis, volume 232, page 65. World Scientific.
Published
2015-07-20
How to Cite
RAMOS NETO, Geovane; BENEVIDES, Marcos; MARQUES, Ricardo; DA ROCHA, Simara; BRAZ JÚNIOR, Geraldo.
Diagnóstico de Câncer de Mama em Mamografias baseado na Análise da Diversidade de Padrões Circulares. In: BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTH (SBCAS), 15. , 2015, Recife.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2015
.
p. 217-220.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2015.10386.
