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In the recent years, there has been a phenomenal growth in the volume of digital mammograms produced in hospitals and medical centers. Thus, there is a need to create efficient access methods or retrieval tools to search, browse and retrieve images from large repositories to aid diagnoses and research. This paper presents a Content Based Medical Image Retrieval (CBMIR) system for mass retrieval in mammograms using a two stage framework. Also, for mass segmentation, a semi-automatic method based on Seed Region Growing approach is proposed. Shape features are extracted at the first stage to find similar shape lesions and the second stage further refines the results by finding similar pathology bearing lesions using texture features. Various shape features used in this study are Compactness, Convexity, Spicularity, Radial Distance (RD) based features, Zernike Moments (ZM) and Fourier Descriptors (FD). The texture of mass lesions is characterized by Gray Level Co-occurrence Matrix (GLCM) features, Gray Level Run Length Matrix (GLRLM) features and Fourier Power Spectrum (FPS) features. In this paper, feature selection is done by Correlation based Feature Selection (CFS) technique to select the best subset of shape and texture features as high dimensionality of feature vector may limit computational efficiency. This study used the IRMA Version of DDSM LJPEG data to evaluate the retrieval performance of various shape and texture features. From the experimental results, it has been found that the proposed CBMIR system using merely the compactness or shape features selected by CFS provided better distinction among four categories of mass shape (Round, Oval, Lobulated and Irregular) at the first stage and FPS based texture features provided better distinction between pathology (Benign and Malignant) at the second stage.
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