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Vasculature Segmentation in MRA Images Using Gradient Compensated Geodesic Active Contours

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Abstract

Precise segmentation of vasculature from three-dimensional (3D) magnetic resonance angiography (MRA) images is playing an important role in image-guided neurosurgery, pre-operation planning and clinical analysis. Active Contour based evolution algorithms are being widely applied to MRA data sets, however existing approaches exhibit some difficulties in extracting tiny parts of the vessels. Our objective is to develop an automated segmentation scheme to accurately extract vasculature of the brain, especially tiny vessels. Inspired by the intrinsic properties of MRA, we have proposed a scheme called the gradient compensated geodesic active contours (GCGAC), which compensates for low gradients near edges of thin vessels. The GCGAC, which is implemented based on level set, has been tested on both synthetic volumetric image and real 3D MRA images. Our experiments show that the introduced gradient compensation can facilitate more accurate segmentation of tiny blood vessels.

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Acknowledgements

We wish to thank Prof. Wang Shih-Chang, Dr. Yeh Ing Berne and other clinicians from the Department of Diagnostic Radiology at National University Hospital of Singapore, specially prof. Wang Shih-Chang and Dr. Yeh Ing Berne, for their kind help and support.

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Correspondence to Ashraf A. Kassim.

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Zonoobi, D., Kassim, A.A. & Shen, W. Vasculature Segmentation in MRA Images Using Gradient Compensated Geodesic Active Contours. J Sign Process Syst Sign Image Video Technol 54, 171–181 (2009). https://doi.org/10.1007/s11265-008-0216-4

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  • DOI: https://doi.org/10.1007/s11265-008-0216-4

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