Document Detail

A fast seed detection using local geometrical feature for automatic tracking of coronary arteries in CTA.
MedLine Citation:
PMID:  25106730     Owner:  NLM     Status:  Publisher    
We propose a fast seed detection for automatic tracking of coronary arteries in coronary computed tomographic angiography (CCTA). To detect vessel regions, Hessian-based filtering is combined with a new local geometric feature that is based on the similarity of the consecutive cross-sections perpendicular to the vessel direction. It is in turn founded on the prior knowledge that a vessel segment is shaped like a cylinder in axial slices. To improve computational efficiency, an axial slice, which contains part of three main coronary arteries, is selected and regions of interest (ROIs) are extracted in the slice. Only for the voxels belonging to the ROIs, the proposed geometric feature is calculated. With the seed points, which are the centroids of the detected vessel regions, and their vessel directions, vessel tracking method can be used for artery extraction. Here a particle filtering-based tracking algorithm is tested. Using 19 clinical CCTA datasets, it is demonstrated that the proposed method detects seed points and can be used for full automatic coronary artery extraction. ROC (receiver operating characteristic) curve analysis shows the advantages of the proposed method.
Dongjin Han; Nam-Thai Doan; Hackjoon Shim; Byunghwan Jeon; Hyunna Lee; Youngtaek Hong; Hyuk-Jae Chang
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2014-7-24
Journal Detail:
Title:  Computer methods and programs in biomedicine     Volume:  -     ISSN:  1872-7565     ISO Abbreviation:  Comput Methods Programs Biomed     Publication Date:  2014 Jul 
Date Detail:
Created Date:  2014-8-9     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8506513     Medline TA:  Comput Methods Programs Biomed     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
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