Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6484247 | Biocybernetics and Biomedical Engineering | 2017 | 12 Pages |
Abstract
The paper presents a method aimed at segmentation of a vascular network in 3D medical data. The method implements an extended version of a vesselness function that considers multiscale image filtering to emphasize vessels of different diameters. This function is combined with a level set approach based on a Chan-Vese model. The proposed method was evaluated on medical images of the brain and hand vasculature. These images were obtained by different modalities, including angio-CT and two MR acquisition protocols. The proposed technique was quantitatively validated for the tree phantom image by assessing segmentation accuracy and for the angio-CT images by estimating diameters of vessel fragments. Two radiologists provided also qualitative evaluation of this approach. It was demonstrated that this method ensures correct segmentation of a vessel tree in the analyzed images. Moreover, it enables detection of thinner vessel branches when compared to single scale vesselness function approaches.
Keywords
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Physical Sciences and Engineering
Chemical Engineering
Bioengineering
Authors
Tomasz Woźniak, MichaÅ Strzelecki, Agata Majos, Ludomir StefaÅczyk,