1 January 2006 Classification of breast masses in mammograms using neural networks with shape, edge sharpness, and texture features
Author Affiliations +
J. of Electronic Imaging, 15(1), 013019 (2006). doi:10.1117/1.2178271
We propose an approach using artificial neural networks to classify masses in mammograms as malignant or benign. Single-layer and multilayer perceptron networks are used in a study on perceptron topologies and training procedures for pattern classification of breast masses. The contours of a set of 111 regions on mammograms related to breast masses and tumors are manually delineated and represented by polygonal models for shape analysis. Ribbons of pixels are extracted around the boundaries of a subset of 57 masses by dilating and eroding the contours. Three shape factors, three measures of edge sharpness, and 14 texture features based on gray-level co-occurrence matrices of the pixels in the ribbons are computed. Several combinations of the features are used with perceptrons of varying topology and training procedures for the classification of benign masses and malignant tumors. The results are compared in terms of the area Az under the receiver operating characteristics curve. Values of Az up to 0.99 are obtained with the shape factors and texture features. However, only feature sets that included at least one shape factor provide consistently high performance with respect to variations in network topology and training.
Túlio C. S. S. André, Rangaraj Mandayam Rangayyan, "Classification of breast masses in mammograms using neural networks with shape, edge sharpness, and texture features," Journal of Electronic Imaging 15(1), 013019 (1 January 2006). http://dx.doi.org/10.1117/1.2178271

Image classification





Breast cancer

Feature extraction

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