Automated Segmentation of Coronary Arteries Based on Statistical Region Growing and Heuristic Decision Method.
The segmentation of coronary arteries is a vital process that helps cardiovascular radiologists detect and quantify stenosis. In this paper, we propose a fully automated coronary artery segmentation from cardiac data volume. The method is built on a statistics region growing together with a heuristi...
| Publicado en: | BioMed Research International Vol. 2016; pp. 1 - 8 |
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| Autores principales: | , , , , , |
| Formato: | equations & formulas tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
10/31/2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=119148384&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 119148384 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 10/31/2016 vid: 2016 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 119148384 119148384 119148384 10.1155/2016/3530251 119148384 ppf: 1 ppct: 7 formats: fmt: @attributes: type: P tig: atl: Automated Segmentation of Coronary Arteries Based on Statistical Region Growing and Heuristic Decision Method. aug: au: Tian, Yun Pan, Yutong Duan, Fuqing Zhao, Shifeng Wang, Qingjun Wang, Wei affil: College of Information Science and Technology, Beijing Normal University, Beijing 100875, China sug: subj: Automation Coronary Vessels Human Statistics Learning Coronary Disease Radiography Diagnostic Imaging Thorax Anatomy and Histology ab: The segmentation of coronary arteries is a vital process that helps cardiovascular radiologists detect and quantify stenosis. In this paper, we propose a fully automated coronary artery segmentation from cardiac data volume. The method is built on a statistics region growing together with a heuristic decision. First, the heart region is extracted using a multi-atlas-based approach. Second, the vessel structures are enhanced via a 3D multiscale line filter. Next, seed points are detected automatically through a threshold preprocessing and a subsequent morphological operation. Based on the set of detected seed points, a statistics-based region growing is applied. Finally, results are obtained by setting conservative parameters. A heuristic decision method is then used to obtain the desired result automatically because parameters in region growing vary in different patients, and the segmentation requires full automation. The experiments are carried out on a dataset that includes eight-patient multivendor cardiac computed tomography angiography (CTA) volume data. The DICE similarity index, mean distance, and Hausdorff distance metrics are employed to compare the proposed algorithm with two state-of-the-art methods. Experimental results indicate that the proposed algorithm is capable of performing complete, robust, and accurate extraction of coronary arteries. pubtype: Academic Journal doctype: equations & formulas tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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