A comparative study of graph search algorithms for segmenting coronary arteries from cine angiography

Benjamin Veasey; Shahab Ghafghazi M.D.; Amir A. Amini

doi:10.1117/12.2511988

15 March 2019 A comparative study of graph search algorithms for segmenting coronary arteries from cine angiography

Benjamin Veasey, Shahab Ghafghazi M.D., Amir A. Amini

Author Affiliations +

Proceedings Volume 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging; 109531T (2019) https://doi.org/10.1117/12.2511988
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Accurate and timely segmentation of coronary vessels in quantitative coronary angiography (QCA) may be important to ensure accurate patient diagnosis. This paper compares three variations of graph search algorithms for use in segmenting coronary arteries in X-ray angiographic images. For comparing these algorithms, we propose a semi-automatic vessel segmentation technique that combines Hessian-based filtering, Gabor filtering, and graph-based search routines for tracing the boundaries1,2. This allows for a more automated procedure by incorporating automatic centerline detection while the use of Gabor filtering promotes a more natural and geometrically continuous border segmentation1. The method requires minimal effort by the user; the only manual input required is a start and end-point along the vessel of interest. Three graph search methods were compared by analyzing the accuracy and computational speed of the segmentations while using each search technique: Dijkstra’s algorithm, a restricted Dijkstra’s algorithm, and the A* search algorithm were compared. The restricted Dijkstra’s and A* approaches reduced the computational time but resulted in low accuracies or outright segmentation failures. As outlined in the paper, Dijkstra’s algorithm results in a superior segmentation with only a marginal increase in computational time.

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Benjamin Veasey, Shahab Ghafghazi M.D., and Amir A. Amini "A comparative study of graph search algorithms for segmenting coronary arteries from cine angiography", Proc. SPIE 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging, 109531T (15 March 2019); https://doi.org/10.1117/12.2511988

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KEYWORDS

Image segmentation

Angiography

Arteries

Image processing algorithms and systems

Image filtering

3D image processing

X-rays

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