Employing topographical height map in colonic polyp measurement and false positive reduction

CT colonography (CTC) is an emerging minimally invasive technique for screening and diagnosing colon cancers. Computer aided detection (CAD) techniques can increase sensitivity and reduce false positives. Inspired by the way radiologists detect polyps via 3D virtual fly-through in CTC, we borrowed the idea from geographic information systems to employ topographical height map in colonic polyp measurement and false positive reduction. After a curvature based filtering and a 3D CT feature classifier, a height map is computed for each detection using a ray-casting algorithm. We design a concentric index to characterize the concentric pattern in polyp height map based on the fact that polyps are protrusions from the colon wall and round in shape. The height map is optimized through a multi-scale spiral spherical search to maximize the concentric index. We derive several topographic features from the map and compute texture features based on wavelet decomposition. We then send the features to a committee of support vector machines for classification. We have trained our method on 394 patients (71 polyps) and tested it on 792 patients (226 polyps). Results showed that we can achieve 95% sensitivity at 2.4 false positives per patient and the height map features can reduce false positives by more than 50%. We compute the polyp height and width measurements and correlate them with manual measurements. The Pearson correlations are 0.74 (p=0.11) and 0.75 (p=0.17) for height and width, respectively.