Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors.
In this paper, we propose a novel method for segmentation of the left ventricle, right ventricle, and myocardium from cine cardiac magnetic resonance images of the STACOM database. Our method incorporates prior shape information in a graph cut framework to achieve segmentation. Poor edge information...
| Publicado en: | Journal of Digital Imaging Vol. 26; no. 4; pp. 721 - 731 |
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| Autor principal: | |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2013
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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=104190876&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104190876 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2013 vid: 26 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104190876 88934671 10.1007/s10278-012-9548-5 NLM23319109 PMC3705018 104190876 ppf: 721 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors. aug: au: Mahapatra, Dwarikanath affil: Department of Computer Science, Swiss Federal Institute of Technology (ETH), CAB F 61.1, Universitätstrasse 6 8092 Zurich Switzerland sug: subj: Magnetic Resonance Imaging Methods Heart Radiography Radiographic Image Interpretation, Computer-Assisted Radiographic Image Enhancement Algorithms Evaluation Comparative Studies P-Value Human ab: In this paper, we propose a novel method for segmentation of the left ventricle, right ventricle, and myocardium from cine cardiac magnetic resonance images of the STACOM database. Our method incorporates prior shape information in a graph cut framework to achieve segmentation. Poor edge information and large within-patient shape variation of the different parts necessitates the inclusion of prior shape information. But large interpatient shape variability makes it difficult to have a generalized shape model. Therefore, for every dataset the shape prior is chosen as a single image clearly showing the different parts. Prior shape information is obtained from a combination of distance functions and orientation angle histograms of each pixel relative to the prior shape. To account for shape changes, pixels near the boundary are allowed to change their labels by appropriate formulation of the penalty and smoothness costs. Our method consists of two stages. In the first stage, segmentation is performed using only intensity information which is the starting point for the second stage combining intensity and shape information to get the final segmentation. Experimental results on different subsets of 30 real patient datasets show higher segmentation accuracy in using shape information and our method's superior performance over other competing methods. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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