Share Email Print

Proceedings Paper

Centerline-based colon segmentation for CAD of CT colonography
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

We developed a fast centerline-based segmentation (CBS) algorithm for the extraction of colon in computer-aided detection (CAD) for CT colonography (CTC). CBS calculates local centerpoints along thresholded components of abdominal air, and connects the centerpoints iteratively to yield a colon centerline. A thick region encompassing the colonic wall is extracted by use of region-growing around the centerline. The resulting colonic wall is employed in our CAD scheme for the detection of polyps, in which polyps are detected within the wall by use of volumetric shape features. False-positive detections are reduced by use of a Bayesian neural network. The colon extraction accuracy of CBS was evaluated by use of 38 clinical CTC scans representing various preparation conditions. On average, CBS covered more than 96% of the visible region of colon with less than 1% extracolonic components in the extracted region. The polyp detection performance of the CAD scheme was evaluated by use of 121 clinical cases with 42 colonoscopy-confirmed polyps 5-25 mm. At a 93% by-polyp detection sensitivity for polyps ≥5 mm, a leave-one-patient-out evaluation yielded 1.4 false-positive polyp detections per CT scan.

Paper Details

Date Published: 16 March 2006
PDF: 8 pages
Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 61445H (16 March 2006); doi: 10.1117/12.653940
Show Author Affiliations
Janne Näppi, Massachusetts General Hospital (United States)
Harvard Medical School (United States)
Hans Frimmel, Uppsala Univ. Hospital (Sweden)
Hiroyuki Yoshida, Massachusetts General Hospital (United States)
Harvard Medical School (United States)

Published in SPIE Proceedings Vol. 6144:
Medical Imaging 2006: Image Processing
Joseph M. Reinhardt; Josien P. W. Pluim, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?