A HYBRID TEXTURAL AND GEOMETRICAL FEATURE EXTRACTION TO REVEAL HIDDEN INFORMATION FROM SUSPICIOUS REGIONS ON MAMMOGRAMS
Abstract
A mammographic feature extraction scheme through textural and geometrical descriptors is examined to implement in a computer-aided diagnosis system for breast cancer diagnosis in this paper. This scheme is verified on a selected subset of suspicious regions (Region of Interest – ROIs) detected on a publicly available mammogram image database constructed by the Mammographic Image Analysis Society. The ROI detection is succeeded using the Chan-Vese active contour modelling after some pre-processing operations which are median filtering, morphological operations, and a region growing method performed for digitization noise reduction, artifact suppression and background removal, and pectoral muscle removal, respectively, applied on mammogram images. Then, a new adaptive convex hull approach is introduced for extracting geometrical descriptors of the ROIs accompanied by the Haralick features extracted from the gray-level co-occurrence matrices for textural description. In addition to geometrical and textural features, a hybrid mammographic feature vector is constructed by concatenating these features. All the three feature vectors are separately utilized to diagnose the ROIs via Random Forest classifier using 5-fold cross-validation. The experimental studies show that the textural features diagnose benignity more specifically and malignancy more accurately; and they are more effective on discriminating healthy ROIs when concatenated with geometrical features. Hence, a feature combination of these three features is proposed for diagnosis. The proposed feature combination is determined to be more effective for more accurate diagnoses of benignity and malignancy.
Keywords
Digital Mammography, Computer-Aided Diagnosis, Feature Extraction, Geometric Descriptor, Textural Descriptor
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