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Optical Engineering

Automatic segmentation of coronary arteries from computed tomography angiography data cloud using optimal thresholding
Author(s): Muhammad Ahsan Ansari; Sammer Zai; Young Shik Moon
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Paper Abstract

Manual analysis of the bulk data generated by computed tomography angiography (CTA) is time consuming, and interpretation of such data requires previous knowledge and expertise of the radiologist. Therefore, an automatic method that can isolate the coronary arteries from a given CTA dataset is required. We present an automatic yet effective segmentation method to delineate the coronary arteries from a three-dimensional CTA data cloud. Instead of a region growing process, which is usually time consuming and prone to leakages, the method is based on the optimal thresholding, which is applied globally on the Hessian-based vesselness measure in a localized way (slice by slice) to track the coronaries carefully to their distal ends. Moreover, to make the process automatic, we detect the aorta using the Hough transform technique. The proposed segmentation method is independent of the starting point to initiate its process and is fast in the sense that coronary arteries are obtained without any preprocessing or postprocessing steps. We used 12 real clinical datasets to show the efficiency and accuracy of the presented method. Experimental results reveal that the proposed method achieves 95% average accuracy.

Paper Details

Date Published: 12 January 2017
PDF: 8 pages
Opt. Eng. 56(1) 013106 doi: 10.1117/1.OE.56.1.013106
Published in: Optical Engineering Volume 56, Issue 1
Show Author Affiliations
Muhammad Ahsan Ansari, Hanyang Univ. (Korea, Republic of)
Sammer Zai, Hanyang Univ. (Republic of Korea)
Young Shik Moon, Hanyang Univ. (Republic of Korea)

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