Utilization Of Digital Image Data For Computer-aided Diagnosis

Abstract

A number of computerized schemes being developed for computer-aided diagnosis (CAD) in our laboratory are reviewed. In distinguishing between normal and abnormal lungs with interstitial infiltrates in chest images, the computerized classification method provided the ROC curve that is comparable to or superior to that obtained by an average radiologist. Our computerized detection schemes indicated truepositive detection rate of approximately 70% for subtle lung nodules in chest radiographs and 90% for subtle clustered microcalcifications in mammograms, although several false positives were detected in each image. The automatically computed outlines of the heart shadows in chest radiographs were very similar to the contours traced by radiologists, and were used to obtain parameters related to the size and area of the projected heart. By using an iterative deconvolution technique, opacified vessels larger than 0.5 mm in DSA images were measured with an accuracy of approximately 0.1mm. The vascular structures in angiograms were tracked accurately and automatically by using a double-square-box region-of-search method.