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Adaptive segmentation of cerebrovascular tree in time-of-flight magnetic resonance angiography

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Abstract

Accurate segmentation of the human vasculature is an important prerequisite for a number of clinical procedures, such as diagnosis, image-guided neurosurgery and pre-surgical planning. In this paper, an improved statistical approach to extracting whole cerebrovascular tree in time-of-flight magnetic resonance angiography is proposed. Firstly, in order to get a more accurate segmentation result, a localized observation model is proposed instead of defining the observation model over the entire dataset. Secondly, for the binary segmentation, an improved Iterative Conditional Model (ICM) algorithm is presented to accelerate the segmentation process. The experimental results showed that the proposed algorithm can obtain more satisfactory segmentation results and save more processing time than conventional approaches, simultaneously.

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Acknowledgments

This paper is supported by “Supported by the National Grand Fundamental Research 973 Program of China under Grant No. 2006CB303000” and “Natural Science Foundation of Shanghai (05ZR14081)”.

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Authors and Affiliations

  1. Department of Computer Science and Engineering Shanghai, Jiaotong University, 800 Dongchuan Road, Min Hang, Shanghai, 200240, People’s Republic of China

    J. T. Hao, M. L. Li & F. L. Tang

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  1. J. T. Hao
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  2. M. L. Li
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  3. F. L. Tang
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Correspondence to J. T. Hao.

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Hao, J.T., Li, M.L. & Tang, F.L. Adaptive segmentation of cerebrovascular tree in time-of-flight magnetic resonance angiography. Med Bio Eng Comput 46, 75–83 (2008). https://doi.org/10.1007/s11517-007-0244-4

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  • DOI: https://doi.org/10.1007/s11517-007-0244-4

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