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.
Due to availability of inexpensive and easily available image capturing devices, the size of digital image collection is increasing rapidly. Thus, there is need to create efficient access methods or retrieval tools to search, browse and retrieve images from large multimedia repositories. More specifically, researchers have been engaged on different ways of retrieving images based on their actual content. In particular, Content Based Image Retrieval (CBIR) systems have attracted considerable research and commercial interest in the recent years. In CBIR, visual features characterizing the image content are color, shape and texture. Currently, texture is used to quantify the image content of medical images as it is the most prominent feature that contains information about the spatial distribution of gray levels and variations in brightness. Various texture models like Haralick’s Spatial Gray Level Co-occurence Matrix (SGLCM), Gray Level Difference Statistics (GLDS), First-order Statistics (FoS), Statistical Feature Matrix (SFM), Law’s Texture Energy Measures (TEM), Fractal features and Fourier Power Spectrum (FPS) features exists in literature. Each of these models visualizes texture in a different way. Retrieval performance depends upon the choice of texture algorithm. Unfortunately, there is no texture model known to work best for encoding texture properties of liver ultrasound images or retrieving most similar images. An experimental comparison of different texture models for Content Based Medical Image Retrieval (CBMIR) is presented in this paper. For the experiments, liver ultrasound image database is used and the retrieval performance of the various texture models is analyzed in detail. The paper concludes with recommendations which texture model performs better for liver ultrasound images. Interestingly, FPS and SGLCM based Haralick’s features perform well for liver ultrasound retrieval and thus can be recommended as a simple baseline for such images.