Curve evolution with a dual shape similarity and its application to segmentation of left ventricle
DC Field | Value | Language |
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dc.contributor.author | Woo, J. | - |
dc.contributor.author | Hong, B.-W. | - |
dc.contributor.author | Ramesh, A. | - |
dc.contributor.author | Germano, G. | - |
dc.contributor.author | Kuo, C.-C.J. | - |
dc.contributor.author | Slomka, P. | - |
dc.date.accessioned | 2021-08-17T02:40:25Z | - |
dc.date.available | 2021-08-17T02:40:25Z | - |
dc.date.issued | 2009-02 | - |
dc.identifier.issn | 1605-7422 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/48456 | - |
dc.description.abstract | Automated image segmentation has been playing a critical role in medical image analysis. Recentl, Level Set methods have shown an efficacy and efficiency in various imaging modalities. In this paper, we present a novel segmentation approach to jointly delineate the boundaries of epi- and endocardium of the left ventricle on the Magnetic Resonance Imaging (MRI) images in a variational framework using level sets, which is in great demand as a clinical application in cardiology. One strategy to tackle segmentation under undesirable conditions such as subtle boundaries and occlusions is to exploit prior knowledge which is specific to the object to segment, in this case the knowledge about heart anatomy. While most left ventricle segmentation approaches incorporate a shape prior obtained by a training process from an ensemble of examples, we exploit a novel shape constraint using an implicit shape prior knowledge, which assumes shape similarity between epi- and endocardium allowing a variation under the Gaussian distribution. Our approach does not demand a training procedure which is usually subject to the training examples and is also laborious and time-consuming in generating the shape prior. Instead, we model a shape constraint by a statistical distance between the shape of epi- and endocardium employing signed distance functions. We applied this technique to cardiac MRI data with quantitative evaluations performed on 10 subjects. The experimental results show the robustness and effectiveness of our shape constraint within a Mumford-Shah segmentation model in the segmentation of left ventricle from cardiac MRI images in comparison with the manual segmentation results. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.title | Curve evolution with a dual shape similarity and its application to segmentation of left ventricle | - |
dc.type | Article | - |
dc.identifier.doi | 10.1117/12.814142 | - |
dc.identifier.bibliographicCitation | Progress in Biomedical Optics and Imaging - Proceedings of SPIE, v.7259 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-71749105372 | - |
dc.citation.title | Progress in Biomedical Optics and Imaging - Proceedings of SPIE | - |
dc.citation.volume | 7259 | - |
dc.type.docType | Conference Paper | - |
dc.subject.keywordAuthor | Dual shape constraint | - |
dc.subject.keywordAuthor | Image segmentation | - |
dc.subject.keywordAuthor | Level Set | - |
dc.subject.keywordAuthor | Prior shape knowledge | - |
dc.subject.keywordPlus | Cardiac MRI | - |
dc.subject.keywordPlus | Clinical application | - |
dc.subject.keywordPlus | Curve evolution | - |
dc.subject.keywordPlus | Dual shape constraint | - |
dc.subject.keywordPlus | Heart anatomy | - |
dc.subject.keywordPlus | Imaging modality | - |
dc.subject.keywordPlus | Left ventricles | - |
dc.subject.keywordPlus | Level Set | - |
dc.subject.keywordPlus | Level Set method | - |
dc.subject.keywordPlus | Manual segmentation | - |
dc.subject.keywordPlus | Medical image analysis | - |
dc.subject.keywordPlus | Mumford-Shah | - |
dc.subject.keywordPlus | Prior knowledge | - |
dc.subject.keywordPlus | Quantitative evaluation | - |
dc.subject.keywordPlus | Shape constraints | - |
dc.subject.keywordPlus | Shape priors | - |
dc.subject.keywordPlus | Shape similarity | - |
dc.subject.keywordPlus | Signed distance function | - |
dc.subject.keywordPlus | Statistical distance | - |
dc.subject.keywordPlus | Training example | - |
dc.subject.keywordPlus | Training procedures | - |
dc.subject.keywordPlus | Training process | - |
dc.subject.keywordPlus | Variational framework | - |
dc.subject.keywordPlus | Digital image storage | - |
dc.subject.keywordPlus | Image analysis | - |
dc.subject.keywordPlus | Image segmentation | - |
dc.subject.keywordPlus | Imaging systems | - |
dc.subject.keywordPlus | Level measurement | - |
dc.subject.keywordPlus | Magnetic resonance imaging | - |
dc.subject.keywordPlus | Resonance | - |
dc.subject.keywordPlus | Medical imaging | - |
dc.description.journalRegisteredClass | scopus | - |
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