Fast vascular skeleton extraction algorithm
2016 (English)In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 76, 67-75 p.Article in journal (Refereed) Published
Vascular diseases are a common cause of death, particularly in developed countries. Computerized image analysis tools play a potentially important role in diagnosing and quantifying vascular pathologies. Given the size and complexity of modern angiographic data acquisition, fast, automatic and accurate vascular segmentation is a challenging task.In this paper we introduce a fully automatic high-speed vascular skeleton extraction algorithm that is intended as a first step in a complete vascular tree segmentation program. The method takes a 3D unprocessed Computed Tomography Angiography (CTA) scan as input and produces a graph in which the nodes are centrally located artery voxels and the edges represent connections between them. The algorithm works in two passes where the first pass is designed to extract the skeleton of large arteries and the second pass focuses on smaller vascular structures. Each pass consists of three main steps. The first step sets proper parameters automatically using Gaussian curve fitting. In the second step different filters are applied to detect voxels - nodes - that are part of arteries. In the last step the nodes are connected in order to obtain a continuous centerline tree for the entire vasculature. Structures found, that do not belong to the arteries, are removed in a final anatomy-based analysis. The proposed method is computationally efficient with an average execution time of 29s and has been tested on a set of CTA scans of the lower limbs achieving an average overlap rate of 97% and an average detection rate of 71%.
Place, publisher, year, edition, pages
Elsevier, 2016. Vol. 76, 67-75 p.
Blood vessels, Centerline tree, CT angiography, Skeleton extraction, Vascular tree, Algorithms, Angiography, Computerized tomography, Curve fitting, Data acquisition, Diagnosis, Extraction, Forestry, Musculoskeletal system, Pathology, Centerlines, Computationally efficient, Computed tomography angiography, Gaussian curve fitting, Vascular tree segmentation, Vascular trees, Trees (mathematics)
IdentifiersURN: urn:nbn:se:kth:diva-177774DOI: 10.1016/j.patrec.2015.06.024ISI: 000375135600009ScopusID: 2-s2.0-84937684883OAI: oai:DiVA.org:kth-177774DiVA: diva2:874729
FunderSwedish Research Council, VR-NT 2014-6153Swedish Heart Lung Foundation, 20130625
QP 2016012015-11-272015-11-252016-05-30Bibliographically approved