Texture Analysis and Artificial Neural Network for Detection of Clustered Microcalcifications on Mammograms

Clustered microcalcifications on X-ray mammograms are an important sign in the detection of breast cancer. This paper quantitatively describes the usefulness of texture analysis methods for the detection of clustered microcalcifications on digitized mammograms. Comparative studies of texture analysis methods are performed for the proposed texture analysis method, called the surrounding region dependence method (SRDM), and the conventional texture analysis methods such as the spatial gray-level dependence method (SGLDM), the gray-level run length method (GLRLM), and the gray-level difference method (GLDM). These methods are applied to classify region of interests (ROIs) into positive ROIs containing clustered microcalcifications and negative ROIs of normal tissues. The database is composed of 72 positive and 100 negative ROI images, which are selected from digitized mammograms with a pixel size of 100 x 100 pm2 and 12 bits per pixel. An ROI is selected as an area of 128 x 128 pixels on the digitized mammograms. A threelayer backpropagation neural network is employed as a classifier. The results of the neural network for texture analysis methods are evaluated by the receiver operating-characteristics (ROC) analysis. From the viewpoint of the classification accuracy and computational complexity, the SRDM is superior to the conventional methods. ,7803-4256-9/97/$10.00 0 1 997 IEEE 199