PROCEEDINGS ARTICLE | March 30, 2007
Proc. SPIE. 6514, Medical Imaging 2007: Computer-Aided Diagnosis
KEYWORDS: Medicine, Computer aided diagnosis and therapy, Detection and tracking algorithms, Computer vision technology, Machine vision, Object recognition, Computed tomography, Computer aided design, Algorithm development, Virtual colonoscopy
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.
