Blood vessel segmentation in volumetric data is a necessary prerequisite in various medical imaging applications. Specifically, when considering the application of automatic lung nodule detection in thoracic CT scans, segmented blood vessels can be used in order to resolve local ambiguities based on global considerations and so improve the performance of lung nodule detection algorithms. In this paper, a novel regulated morphology approach to fuzzy shape analysis is described in the context of blood vessel extraction in thoracic CT scans. The fuzzy shape representation is obtained by using regulated morphological operations. Such a representation is necessary due to noise present in the data and due to the discrete nature of the volumetric data produced by CT scans, and particularly the interslice spacing. Regulated morphological operations are a generalization of ordinary morphological operations which relax the extreme strictness inherent to ordinary morphological operations. Based on constraints of collinearity, size, and global direction, a tracking algorithm produces a set of connected trees representing blood vessels and nodules in the volume. The produced tree structures are composed of fuzzy spheres in which the degree of object membership is proportional to the ratio between the occupied volume and the volume of the discrete sphere encompassing it. The performance of the blood vessel extraction algorithm described in the paper is evaluated based on a distance measure between a known blood vessel structure and a recovered one. As the generation of synthetic data for which the true vessel network is known may not be sufficiently realistic, our evaluation is based on different versions of real data corrupted by multiplicative Gaussian noise.

Date Published: 12 May 2004
PDF: 9 pages
Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); doi: 10.1117/12.533175

Published in SPIE Proceedings Vol. 5370:
Medical Imaging 2004: Image Processing
J. Michael Fitzpatrick; Milan Sonka, Editor(s)