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6 June 2000 Segmentation and classification of mammographic masses
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Abstract
We propose a method for detection of masses in mammographic images that employs pyramidal or hierarchical decomposition and Gaussian filtering operations as pre-processing steps. A procedure is then developed to segment the mass portions by establishing gradual intensity links from the central portions of masses into the surrounding areas in the image. The proposed mass detection algorithm was tested with 39 cases (28 benign and 11 malignant) selected from the Mammographic Image Analysis Society database. The technique achieved a success rate of 91% in detecting the malignant tumors and 68% in detecting the benign masses in the test set. The segmented mass portions were evaluated in terms of their benign versus malignant discriminant capabilities by computing two gradient- based features and texture features based on gray-level co- occurrence matrices (GCMs). The features were computed using a ribbon of pixels across the mass boundaries. The GCM-based texture features in combination with the gradient-based features resulted in the best benign versus malignant classification of the mass regions segmented by the proposed algorithm, with an area of 0.84 under the receiver operating characteristics curve.
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Naga R. Mudigonda, Rangaraj M. Rangayyan, and J. E. Leo Desautels "Segmentation and classification of mammographic masses", Proc. SPIE 3979, Medical Imaging 2000: Image Processing, (6 June 2000); https://doi.org/10.1117/12.387692
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