| Weighted local variance-based edge detection and its application to vascular segmentation in magnetic resonance angiography. | |
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MedLine Citation:
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PMID: 17896595 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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Accurate detection of vessel boundaries is particularly important for a precise extraction of vasculatures in magnetic resonance angiography (MRA). In this paper, we propose the use of weighted local variance (WLV)-based edge detection scheme for vessel boundary detection in MRA. The proposed method is robust against changes of intensity contrast of edges and capable of giving high detection responses on low contrast edges. These robustness and capabilities are essential for detecting the boundaries of vessels in low contrast regions of images, which can contain intensity inhomogeneity, such as bias field, interferences induced from other tissues, or fluctuation of the speed related vessel intensity. The performance of the WLV-based edge detection scheme is studied and shown to be able to return strong and consistent detection responses on low contrast edges in the experiments. The proposed edge detection scheme can be embedded naturally in the active contour models for vascular segmentation. The WLV-based vascular segmentation method is tested using MRA image volumes. It is experimentally shown that the WLV-based edge detection approach can achieve high-quality segmentation of vasculatures in MRA images. |
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Authors:
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Max W K Law; Albert C S Chung |
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Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: IEEE transactions on medical imaging Volume: 26 ISSN: 0278-0062 ISO Abbreviation: IEEE Trans Med Imaging Publication Date: 2007 Sep |
Date Detail:
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Created Date: 2007-09-27 Completed Date: 2007-11-13 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 8310780 Medline TA: IEEE Trans Med Imaging Country: United States |
Other Details:
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Languages: eng Pagination: 1224-41 Citation Subset: IM |
Affiliation:
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Lo Kwee-Seong Medical Image Analysis Laboratory, Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong. maxlawwk@cse.ust.hk |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms* Analysis of Variance Artificial Intelligence* Cerebral Arteries / anatomy & histology* Computer Simulation Humans Image Enhancement / methods Image Interpretation, Computer-Assisted / methods* Magnetic Resonance Angiography / methods* Models, Neurological Models, Statistical Pattern Recognition, Automated / methods* Reproducibility of Results Sensitivity and Specificity Subtraction Technique* |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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