Content-based mammogram retrieval using wavelet based complete-LBP and K-means clustering for the diagnosis of breast cancer
Vibhav Prakash Singh, Rajeev Srivastava · International Journal of Hybrid Intelligent Systems · 2017
Early diagnosis of breast cancer can improve the survival rate by detecting cancer at initial stage. In this paper, an efficient content-based mammogram retrieval system is proposed, which helps in early diagnosis of breast cancer by classifying the current case mammogram and retrieving similar pas t cases mammograms already annotated by diagnostic descriptions and treatment results. The proposed steps include cropping of mammograms for finding the region of interest (ROI), feature extraction using wavelet based-complete local binary pattern (W-CLBP) and K-means clustering. Strong texture characteristics of the mammogram are captured using CLBP to all detailed coefficients (LH, HL, and HH) from two level decomposed 2 Dimensional-discrete wavelet transform (2D-DWT) of ROI. Further, K-means generates the clusters based on this texture similarity of mammograms, and query mammogram 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 mammogram is searched only in small sub-set depending upon the 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 that the proposed method has better say with respect to other four variants of texture features.