DETECTION OF CLUSTERED MICROCALCIFICATIONS ON MAMMOGRAMS USING SURROUNDING REGION DEPENDENCE METHOD AND ARTIFICIAL NEURAL-NETWORK

Clustered microcalcifications on X-ray mammograms are an important sig n in the detection of breast cancer. A statistical texture analysis me thod, called the surrounding region dependence method (SRDM), is propo sed for the detection of clustered microcalcifications on digitized ma mmograms. The SRDM is based on the second-order histogram in two surro unding regions. This method defines four textural features to classify region of interests (ROIs) into positive ROIs containing clustered mi crocalcifications and negative ROIs of normal tissues. The database is composed of 64 positive and 76 negative ROI images, which are selecte d 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 ty pes, a three-layer backpropagation neural network is employed as a cla ssifier. A segmentation of individual microcalcifications is also prop osed to show their morphologies. The classification performance of the proposed method is evaluated by using the round-robin method and a fr ee-response receiver operating-characteristics (FROC) analysis. A rece iver operating-characteristics (ROC) analysis is employed to present t he results of the round-robin testing for the case of several hidden n eurons. The area under the ROC curve, A(z), is 0.997, which is achieve d in the case of 4 hidden neurons. The FROG analysis is performed on 2 0 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 sensitivit y of more than 90% is obtained with a low false-positive (FP) detectio n rate of 0.67 per cropped image.