Threshold segmentation algorithm for automatic extraction of cerebral vessels from brain magnetic resonance angiography images

•A novel segmentation algorithm is proposed to extract cerebral vessels from brain magnetic resonance angiography (MRA) images.•The vessel segmentation algorithm is fast and fully automatic.•The performance of the threshold segmentation is acceptable.•The segmentation method may be used for three-di...

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Bibliographic Details
Published in:Journal of neuroscience methods Vol. 241; pp. 30 - 36
Main Authors: Wang, Rui, Li, Chao, Wang, Jie, Wei, Xiaoer, Li, Yuehua, Zhu, Yuemin, Zhang, Su
Format: Journal Article
Language:English
Published: Netherlands Elsevier B.V 15.02.2015
Elsevier
Subjects:
Adult
Aged
Aged, 80 and over
Algorithms
Brain - blood supply
Brain - diagnostic imaging
Cerebral Arteries - diagnostic imaging
Cerebral Veins - diagnostic imaging
Cerebral vessels
Cerebrovascular Circulation - physiology
Engineering Sciences
Female
Humans
Image Enhancement - methods
Image Enhancement - standards
Imaging, Three-Dimensional - methods
Imaging, Three-Dimensional - standards
Magnetic resonance angiography
Magnetic Resonance Angiography - methods
Magnetic Resonance Angiography - standards
Male
Middle Aged
Radiography
Signal and Image processing
Statistical distribution
Threshold segmentation
Cerebral vessels
Statistical distribution
Magnetic resonance angiography
Threshold segmentation
ISSN:0165-0270, 1872-678X, 1872-678X
Online Access:Get full text
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Summary:•A novel segmentation algorithm is proposed to extract cerebral vessels from brain magnetic resonance angiography (MRA) images.•The vessel segmentation algorithm is fast and fully automatic.•The performance of the threshold segmentation is acceptable.•The segmentation method may be used for three-dimensional visualization and volumetric quantification of cerebral vessels. Cerebrovascular segmentation plays an important role in medical diagnosis. This study was conducted to develop a threshold segmentation algorithm for automatic extraction and volumetric quantification of cerebral vessels on brain magnetic resonance angiography (MRA) images. The MRA images of 10 individuals were acquired using a 3 Tesla MR scanner (Intera-achieva SMI-2.1, Philips Medical Systems). Otsu's method was used to divide the brain MRA images into two parts, namely, foreground and background regions. To extract the cerebral vessels, we performed the threshold segmentation algorithm on the foreground region by comparing two different statistical distributions. Automatically segmented vessels were compared with manually segmented vessels. Different similarity metrics were used to assess the changes in segmentation performance as a function of a weighted parameter w used in segmentation algorithm. Varying w from 2 to 100 resulted in a false positive rate ranging from 117% to 3.21%, and a false negative rate ranging from 8.23% to 28.97%. The Dice similarity coefficient (DSC), which reflected the segmentation accuracy, initially increased and then decreased as w increased. The suggested range of values for w is [10, 20] given that the maximum DSC (e.g., DSC=0.84) was obtained within this range. The performance of our method was validated by comparing with manual segmentation. The proposed threshold segmentation method can be used to accurately and efficiently extract cerebral vessels from brain MRA images. Threshold segmentation may be used for studies focusing on three-dimensional visualization and volumetric quantification of cerebral vessels.
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ISSN:0165-0270
1872-678X
1872-678X
DOI:10.1016/j.jneumeth.2014.12.003