Detection of clustered microcalcifications on mammograms using surrounding region dependence method and artificial neural network

Abstract

Clustered microcalcifications on X-ray mammograms are an important sign in the detection of breast cancer. A statistical texture analysis method, called the surrounding region dependence method (SRDM), is proposed for the detection of clustered microcalcifications on digitized mammograms. The SRDM is based on the second-order histogram in two surrounding regions. This method defines four textural features to classify region of interests (ROIs) into positive ROIs containing clustered microcalcifications and negative ROIs of normal tissues. The database is composed of 64 positive and 76 negative ROI images, which are selected from digitized mammograms with a pixel size of 100 x 100 mu m(2) and 12 bits per pixel. An ROI is selected as an area of 128 x 128 pixels on the digitized mammograms. In order to classify ROIs into the two types, a three-layer backpropagation neural network is employed as a classifier. A segmentation of individual microcalcifications is also proposed to show their morphologies. The classification performance of the proposed method is evaluated by using the round-robin method and a free-response receiver operating-characteristics (FROC) analysis. A receiver operating-characteristics (ROC) analysis is employed to present the results of the round-robin testing for the case of several hidden neurons. The area under the ROC curve, A(z), is 0.997, which is achieved in the case of 4 hidden neurons. The FROG analysis is performed on 20 cropped images. A cropped image is selected as an area of 512 x 512 pixels on the digitized mammograms. In terms of the FROG, a sensitivity of more than 90% is obtained with a low false-positive (FP) detection rate of 0.67 per cropped image.