[Show abstract][Hide abstract] ABSTRACT: A new algorithm for massive lesion detection in mammography is presented. The algorithm consists in three main steps: 1) reduction of the dimension of the image to be processed through the identification of regions of interest (roi) as candidates for massive lesions; 2) characterization of the RoI by means of suitable feature extraction; 3) pattern classification through supervised neural networks. Suspect regions are detected by searching for local maxima of the pixel grey level intensity. A ring of increasing radius, centered on a maximum, is considered until the mean intensity in the ring decreases to a defined fraction of the maximum. The ROIS thus obtained are described by average, variance, skewness and kurtosis of the intensity distributions at different fractions of the radius. A neural network approach is adopted to classify suspect pathological and healthy pattern. The software has been designed in the framework of the INFN (Istituto Nazionale Fisica Nucleare) research project GPCALMA (Grid Platform for Calma) which recruits physicists and radiologists from different Italian Research Institutions and hospitals to develop software for breast cancer detection.
[Show abstract][Hide abstract] ABSTRACT: In this article we present a classification system for an automatic detection of masses in digitized mammographic images. The systems consists in three main processing levels: a) image segmentation for the localization of regions of interest (ROIs); b) ROI characterization by means of textural features computed from the Gray Tone Spatial Dependence Matrix (GTSDM), containing second order spatial statistics information on the pixel grey level intensity; c) ROI classification by means of a neural network, with supervision provided by the radiologist’s diagnosis. The CAD system was developed and evaluated using a database of N<sub>I</sub> = 3369 mammographic images: the breakdown of the cases was N<sub>In</sub> = 2307 negative images, and N<sub>Ip</sub> = 1062 pathological (or positive) images, containing at least one confirmed mass, as diagnosed by an expert radiologist. To examine the performance of the overall CAD system, receiver operating characteristic (ROC) and free-response ROC (FROC) analysis were employed. The area under the ROC curve was found to be A<sub>z</sub> = 0.78 ± 0.008 for ROI-based classification. When evaluating the accuracy of the CAD against the radiologist-drawn boundaries, 4.23 false positive per image (FPpI) are found at 80% mass sensitivity.