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Proceedings Paper

A distance-field-based approach in generating cross-sections for 2-D vessel quantification
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Paper Abstract

For accurate determination of thickness-profile in vessel quantification, it is important to find appropriate vessel cross-sections. To obtain vessel cross-sections, a centerline-based approach has been widely used, but it has several inherent problems causing improper cross-sections. First, this approach cannot define cross-sections in a unique way. Second, cross-sections are sensitive to the degree of smoothness of a detected vessel centerline. Third, a small variation in a centerline causes a considerable change in the resultant cross-sections and this phenomenon brings about improper cross-sections in the abnormal vessel of asymmetric structure. Finally, wrong cross-sections may be detected due to the intersection with the other cross-sections in a region of high curvature. In this paper, instead of a centerline, we propose and adopt a complementary geodesic distance field. Then, we detect a sequence of equidistant lines by using the proposed distance field. Finally, we determine cross-sections by refining the obtained equidistant lines. Due to the prospective properties of the proposed distance field, we can alleviate all of the conventional problems and obtain the cross-sections more proper for vessel quantification. Through the intensive simulation using various 2-D synthesized images, we prove that the proposed method provides non-intersecting cross-sections which are insensitive to local variation of geometrical shapes in abnormal vessels.

Paper Details

Date Published: 14 April 2005
PDF: 12 pages
Proc. SPIE 5746, Medical Imaging 2005: Physiology, Function, and Structure from Medical Images, (14 April 2005); doi: 10.1117/12.595143
Show Author Affiliations
Dong-Goo Kang, Korea Advanced Institute of Science and Technology (South Korea)
Jong Beom Ra, Korea Advanced Institute of Science and Technology (South Korea)

Published in SPIE Proceedings Vol. 5746:
Medical Imaging 2005: Physiology, Function, and Structure from Medical Images
Amir A. Amini; Armando Manduca, Editor(s)

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