In this paper a robust regions-of-suspicion (ROS) diagnosis system on mammograms, recognizing all types of abnormalities is presented and evaluated. A new type of statistical descriptors, based on Independent Component Analysis (ICA), derive the source regions that generate the observed ROS in mammograms. The reduced set of linear transformation coefficients, estimated from ICA after principal component analysis (PCA), compose the features vector that describes the observed regions in an effective way. The ROS are diagnosed using support-vector-machines (SVMs) with polynomial and radial basis function kernels. Taking into account the small number of training data, the PCA preprocessing step reduces the dimensionality of the features vector and consequently improves the classification accuracy of the SVM classifier. Extensive experiments using the Mammographie Image Analysis Society (MIAS) database have given high recognition accuracy above 87%. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Koutras, A., Christoyianni, I., Georgoulas, G., & Dermatas, E. (2006). Computer aided classification of mammographic tissue using independent component analysis and support vector machines. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4132 LNCS-II, pp. 568–577). Springer Verlag. https://doi.org/10.1007/11840930_59
Mendeley helps you to discover research relevant for your work.