Computer-aided detection of colonic polyps with level set-based adaptive convolution in volumetric mucosa to advance CT colonography toward a screening modality

As a promising second reader of computed tomographic colonography (CTC) screening, the computer-aided detection (CAD) of colonic polyps has earned fast growing research interest. In this paper, we present a CAD scheme to automatically detect colonic polyps in CTC images. First, a thick colon wall representation, ie, a volumetric mucosa (VM) with several voxels wide in general, was segmented from CTC images by a partial-volume image segmentation algorithm. Based on the VM, we employed a level set-based adaptive convolution method for calculating the first- and second-order spatial derivatives more accurately to start the geometric analysis. Furthermore, to emphasize the correspondence among different layers in the VM, we introduced a middle-layer enhanced integration along the image gradient direction inside the VM to improve the operation of extracting the geometric information, like the principal curvatures. Initial polyp candidates (IPCs) were then determined by thresholding the geometric measurements. Based on IPCs, several features were extracted for each IPC, and fed into a support vector machine to reduce false positives (FPs). The final detections were displayed in a commercial system to provide second opinions for radiologists. The CAD scheme was applied to 26 patient CTC studies with 32 confirmed polyps by both optical and virtual colonoscopies. Compared to our previous work, all the polyps can be detected successfully with less FPs. At the 100% by polyp sensitivity, the new method yielded 3.5 FPs/dataset

On normalized convolution to measure curvature features for automatic polyp detection

Abstract. Early removal of polyps has proven to decrease the incidence of colon cancer. We aim to increase the sensitivity of the screening by automatic detection of polyps. It requires accurate measurement of the colon wall curvature. This paper describes a new method which computes the curvatures using space-variant derivative operators in a strip along the edge of the colon. It optimizes the trade-off between noise reduction and mixing of adjacent image structures. The derivative operators incorporate an applicability function for regularization and interpret the strips as confidence measure; certain inside and uncertain outside. To that purpose the technique of normalized convolution is utilized and adapted to allow a local Taylor expansion of the image signal. A special scheme to compute the confidence values is also presented.