Content-Based Mammogram Retrieval Using K-Means Clustering And Local Binary Pattern

Early diagnosis of breast cancer can improve the survival rate by detecting cancer at initial stage. In this paper, an efficient computer-based mammogram retrieval system is proposed, which helps in early diagnosis of breast cancer by comparing the current case with past cases. The proposed steps include cropping of mammograms, feature extraction using local binary pattern (LBP) and k-mean clustering. Using LBP, k-mean generates the clusters based on the visual similarity of mammograms. Further, query image features are matched with all cluster representatives to find the closest cluster. Finally, images are retrieved from this closest cluster using Euclidean distance similarity measure. So, at the searching time the query image is searched only in small subset depending upon cluster size and is not compared with all the images in the database, reflects a superior response time with good retrieval performances. Experiments on benchmark mammography image analysis society (MIAS) database confirm the effectiveness of this work.