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• W2123015183 abstract "<para xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> 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. </para>" @default.
• W2123015183 created "2016-06-24" @default.
• W2123015183 creator A5028163457 @default.
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• W2123015183 date "2007-09-01" @default.
• W2123015183 modified "2023-10-05" @default.
• W2123015183 title "Weighted Local Variance-Based Edge Detection and Its Application to Vascular Segmentation in Magnetic Resonance Angiography" @default.
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• W2123015183 doi "https://doi.org/10.1109/tmi.2007.903231" @default.
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