NDLI: Automated Detection and Segmentation of Large Lesions in CT Colonography

Content Provider	IEEE Xplore Digital Library
Author	Grigorescu, S.E. Nevo, S.T. Liedenbaum, M.H. Truyen, R. Stoker, J. van Vliet, L.J. Vos, F.M.
Copyright Year	1964
Abstract	Computerized tomographic colonography is a minimally invasive technique for the detection of colorectal polyps and carcinoma. Computer-aided diagnosis (CAD) schemes are designed to help radiologists locating colorectal lesions in an efficient and accurate manner. Large lesions are often initially detected as multiple small objects, due to which such lesions may be missed or misclassified by CAD systems. We propose a novel method for automated detection and segmentation of all large lesions, i.e., large polyps as well as carcinoma. Our detection algorithm is incorporated in a classical CAD system. Candidate detection comprises preselection based on a local measure for protrusion and clustering based on geodesic distance. The generated clusters are further segmented and analyzed. The segmentation algorithm is a thresholding operation in which the threshold is adaptively selected. The segmentation provides a size measurement that is used to compute the likelihood of a cluster to be a large lesion. The large lesion detection algorithm was evaluated on data from 35 patients having 41 large lesions (19 of which malignant) confirmed by optical colonoscopy. At five false positive (FP) per scan, the classical system achieved a sensitivity of 78%, while the system augmented with the large lesion detector achieved 83% sensitivity. For malignant lesions, the performance at five FP/scan was increased from 79% to 95%. The good results on malignant lesions demonstrate that the proposed algorithm may provide relevant additional information for the clinical decision process.
Sponsorship	IEEE Engineering in Medicine and Biology Society
Page Count	10
File Size	1087726
Starting Page	675
Ending Page	684
File Format	PDF
ISSN	00189294
Volume Number	57
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-03-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Lesions Virtual colonoscopy Cancer Design automation Detection algorithms Tomography Colonography Minimally invasive surgery Colonic polyps Computer aided diagnosis $LH$ histogram Carcinomas computer-aided detection computerized tomographic (CT) colonography (CTC) image segmentation
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

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