Abstract
After pre-processing and segmenting suspicious masses in mammographies based on the Top-Hat and Markov Random Fields methods, we developed a mass-detection algorithm that uses gray level co-occurrence matrices, gray level difference statistics, gray level run length statistics, shape descriptors and intensity parameters as the entry of a vector support machine classifier. During the classification process we test up to 63 image features, keeping the 35 most important and obtaining 85% of accuracy score.
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Arias, J.A., Rodríguez, V., Miranda, R. (2013). Meaningful Features for Computerized Detection of Breast Cancer. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2013. Lecture Notes in Computer Science, vol 8259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41827-3_25
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DOI: https://doi.org/10.1007/978-3-642-41827-3_25
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