Share Email Print

Proceedings Paper

A novel algorithm for polyp detection using Eigen decomposition of Hessian-matrix for CT colonography CAD: validation with physical phantom study
Author(s): June-Goo Lee; Se Hyung Kim; Jong Hyo Kim; Namkug Kim; Jin Young Choi; KwangGi Kim; Jong-Mo Seo
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Hessian matrix is the square matrix of second partial derivatives of a scalar-valued function and is well known for object recognition in computer vision and medical shape analysis. Previous curvature based polyp detection algorithms generate myriad of false positives. Hessian-matrix based method, however, is more sensitive to local shape features, so easily reduce false positives. Calculation of Hessian matrix on 3D CT data and Eigen decomposition of the matrix gives three Eigen values and vectors at each voxel. Using these Eigen values, we can figure out which type of intensity structures (blob, line, and sheet-like) is on the given voxel. We focus on detecting blob-like object automatically. In the inner colonic wall structures, blob-like, line-like, and sheet-like objects represent polyps, folds and wall, respectively. In addition, to improve the performance of the algorithm, Gaussian blurring factor and shape threshold parameters are optimized. Before Hessian matrix calculation, smoothing the given region using Gaussian kernel with small deviation is necessary to enhance local intensity structures. To optimize the parameters and validate this method, we have produced anthropomorphic pig phantoms. Fourteen phantoms with 103 polyps (16 polyps <6mm, 87 >= 6mm) were used. CT scan was performed with 1mm slice thickness. Our detection algorithm found 84 polyps (81.6%) correctly. Average number of false positives is 7.9 at each CT scan. This results show that our algorithm is clinically applicable for polyp detection, because of high sensitivity and relatively low false positive detections.

Paper Details

Date Published: 30 March 2007
PDF: 7 pages
Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 65142K (30 March 2007); doi: 10.1117/12.709175
Show Author Affiliations
June-Goo Lee, Seoul National Univ. College of Medicine (South Korea)
Se Hyung Kim, Seoul National Univ. College of Medicine (South Korea)
Jong Hyo Kim, Seoul National Univ. College of Medicine (South Korea)
Namkug Kim, Seoul National Univ. (South Korea)
Jin Young Choi, Seoul National Univ. College of Medicine (South Korea)
KwangGi Kim, Seoul National Univ. College of Medicine (South Korea)
Jong-Mo Seo, Seoul National Univ. College of Medicine (South Korea)

Published in SPIE Proceedings Vol. 6514:
Medical Imaging 2007: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

© SPIE. Terms of Use
Back to Top