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

• Main Authors: Hongbin Zhu, Chaijie Duan, Perry Pickhardt, Su Wang, et al.
• Format: Article
• Language: English
• Published: Dove Medical Press 2009-03-01
• Series: Cancer Management and Research
• Online Access: http://www.dovepress.com/computer-aided-detection-of-colonic-polyps-with-level-set-based-adapti-a2935

Hongbin Zhu1, Chaijie Duan1, Perry Pickhardt2, Su Wang1, Zhengrong Liang1,31Department of Radiology, 3Department of Computer Science, State University of New York, Stony Brook, NY, USA; 2Department of Radiology, University of Wisconsin Medical School, Madison, WI, USAAbstract: 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. Keywords: colonic polyps, level set, adaptive convolution, middle-layer enhanced integration, volumetric mucosa, support vector machine, computer-aided detection, CT colonography