| A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes. | |
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MedLine Citation:
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PMID: 19818675 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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Vascular diseases are among the most important public health problems in developed countries. Given the size and complexity of modern angiographic acquisitions, segmentation is a key step toward the accurate visualization, diagnosis and quantification of vascular pathologies. Despite the tremendous amount of past and on-going dedicated research, vascular segmentation remains a challenging task. In this paper, we review state-of-the-art literature on vascular segmentation, with a particular focus on 3D contrast-enhanced imaging modalities (MRA and CTA). We structure our analysis along three axes: models, features and extraction schemes. We first detail model-based assumptions on the vessel appearance and geometry which can embedded in a segmentation approach. We then review the image features that can be extracted to evaluate these models. Finally, we discuss how existing extraction schemes combine model and feature information to perform the segmentation task. Each component (model, feature and extraction scheme) plays a crucial role toward the efficient, robust and accurate segmentation of vessels of interest. Along each axis of study, we discuss the theoretical and practical properties of recent approaches and highlight the most advanced and promising ones. |
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Authors:
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David Lesage; Elsa D Angelini; Isabelle Bloch; Gareth Funka-Lea |
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Publication Detail:
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Type: Journal Article; Review Date: 2009-08-12 |
Journal Detail:
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Title: Medical image analysis Volume: 13 ISSN: 1361-8423 ISO Abbreviation: Med Image Anal Publication Date: 2009 Dec |
Date Detail:
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Created Date: 2009-11-02 Completed Date: 2010-02-02 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9713490 Medline TA: Med Image Anal Country: Netherlands |
Other Details:
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Languages: eng Pagination: 819-45 Citation Subset: IM |
Affiliation:
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Siemens Corporate Research, Imaging and Visualization Dept., Princeton, NJ, USA. |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms* Angiography / methods* Artificial Intelligence* Humans Image Enhancement / methods Image Interpretation, Computer-Assisted / methods* Imaging, Three-Dimensional / methods* Pattern Recognition, Automated / methods* Reproducibility of Results Sensitivity and Specificity Subtraction Technique* |
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