Automated Detection of Peripheral Arteries in CTA Datasets

Peripheral artery disease is a chronic disease that manifests in insufficient blood supply to the legs due to narrowing of the arteries. Fully automated detection, segmentation and measurement of stenosis of peripheral vessels from CTA datasets would be highly desirable but has yet to be realized. A key component of this procedure is the development of an automated and accurate method for the segmentation of the peripheral vessel, which would be a major step towards the automated detection of stenosis. We propose a Computer Aided Detection (CAD) algorithm, with which to detect and segment the peripheral vessels directly from 3D data. In order to create a good delineation of arteries in the image, and as to improve the sensitivity for detection and measurement of stenosis, a differential geometry-based approach is employed. This approach serves as an enhancement filter and, further, provides information about the geometry of the structures in the image: the tubular objects representing the interest (arteries). Having enhanced the arteries, a 3D region growing method is employed, utilizing voxel-based geometrical features. With this proposed region growing method the initial seed point is represented by the common iliac arteries junction, and it is thus automatically selected. The method has been successfully implemented on 15 datasets and the evaluation was carried out by the visual judgment of 2 experienced radiologists.