Poster Sessions

IMAG -31

Shijun Wang

S. Wang, J. Yao, R. Summers, N. Petrick

Combining heterogeneous features for colonic polyp detection in CTC based on semi-definite programming

Computed tomographic colonography (CTC) combined with a computer aided detection system provides a feasible combination for improving colonic polyps detection and increasing the use of CTC for colon cancer screening. To distinguish true polyps from false positives, various features extracted from polyp candidates have been proposed. In this paper, we propose a new set of shape descriptors for polyp candidates based on statistical curvature information. Then in order to make full use of the traditional features (defined as group A) and the new features (group B) which are highly heterogeneous, we employed a multiple kernel learning method based on semi-definite programming to identify an optimized classification kernel based on the combined set of features. We did leave-one-patient-out test on a CTC dataset which contained scans from 50 patients. Experimental results show that a support vector machine (SVM) based on the combined feature set and the semi-definite optimization kernel achieved higher FROC performance compared to SVMs using the two groups of features separately. At a false positive per patient rate of 7, the sensitivity using the combined features improved from 0.78 (Group A) and 0.73 (Group B) to 0.82 (p<0.01).