An efficient content based image retrieval for normal and abnormal mammograms

Diagnosis of breast cancer from mammograms is a crucial task. CBIR can support radiologists in their decision to retrieve similar mammograms out of a database to compare the past cases with current case. Pectoral muscle, labels, and artifacts, present in mammograms can bias the detection procedures. So extractions of these are an essential preprocessing step in the process of CAD. In this paper, an efficient content based image retrieval system is developed for normal and abnormal classes of mammograms. The pre-processing steps include are artifact suppression using CCL and Morphological operation, automatic pectoral muscle removal, and image enhancement using CLAHE. After pre-processing, we segment images using modified region growing algorithm, and using this segmented image, Histogram based statistical, Shape, Wavelet and Gabor features are extracted. Finally, images are retrieved using Euclidean distance similarity measure. Experiments on benchmark database confirm that the proposed segmentation and retrieval framework performs, encouraging than Fuzzy c mean, Ostu, and Region Growing based segmentation and retrieval approaches.