Increasing computer-aided detection specificity by projection features for CT colonography

Increasing computer-aided detection specificity by projection features for CT colonography.

PURPOSE A large number of false positives (FPs) generated by computer-aided detection (CAD) schemes is likely to distract radiologists' attention and decrease their interpretation efficiency. This study aims to develop projection-based features which characterize true and false positives to increase the specificity while maintaining high sensitivity in detecting colonic polyps. METHODS In thi...

Polyp Segmentation Method for CT Colonography Computer Aided Detection

We have developed a new method employing the Canny edge detector and Radon transformation to segment images of polyp candidates for CT colonography (CTC) computer aided polyp detection and obtain features useful for distinguishing true polyps from false positive detections. The technique is applied to two-dimensional subimages of polyp candidates selected using various 3-D shape and curvature c...

Projection-based features for reducing false positives in computer-aided detection of colonic polyps in CT colonography [7624-10]

A large number of false positives (FPs) generated by computer-aided detection schemes is likely to distract radiologists’ attention and decreases their interpretation efficiency. Therefore, it is desirable to reduce FPs as many as possible to increase the detection specificity while maintaining the high detection sensitivity. In this paper, several features are extracted from the projected imag...

Computer Aided Detection of Polyps in CT Colonography

PURPOSE A massive-training artificial neural network (MTANN) has been developed for the reduction of false positives (FPs) in computer-aided detection (CADe) of polyps in CT colonography (CTC). A major limitation of the MTANN is the long training time. To address this issue, the authors investigated the feasibility of two state-of-the-art regression models, namely, support vector regression (SV...

Computer Aided Detection of Potential Colorectal Neoplasia at CT Colonography