Texture Feature-based Automatic Breast Tissue Classification in Digitized Mammograms

摘要

Computer-aided diagnostic (CAD) systems play a crucial role in facilitating the detection of mammographic abnormalities (e.g. microcalcifications and masses) for radiologists. However, it has been shown that the sensitivity of these systems decreases significantly as the density of the breast tissue is increased. In addition, breast tissue density is widely accepted as an important risk factor in development of breast cancer. Automatic breast tissue classification will assist the CAD system to detect the breast cancer very efficiently and quickly. In this paper, we proposed an automatic breast tissue classification method based on textural analysis of mammographic images. The proposed method consists of three steps: 1) segmentation of the mammogram into breast region by x-ray labelling and pectoral muscle removal; 2) extraction of textural features based on intensity histogram; 3) use of two different classification methods (a proposed nearest neighbour majority selection and k-nearest neighbour classifier) in determining the tissue types based on the mini-MIAS classification protocol. The evaluation was done on 120 randomly selected images while using 30 images as training data. An overall correct classification rate of 71% was achieved using only six textural features that is very much promising in the direction of automatic breast cancer diagnosis and detection compare to other existing methods.