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Spatial continuity incorporated multi-attribute fuzzy clustering algorithm for blood vessels segmentation

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Abstract

A three-dimensional representation of vasculature can be extremely important in image-guided neurosurgery, pre-surgical planning. In this paper, a spatial continuity incorporated multi-attribute fuzzy clustering algorithm (MAFCM_S) is proposed to segment entire blood vessels from TOF MRA images. This clustering method takes both the intensity information and the geometrical information into account, while most of the current clustering methods only deal with the former. In this method, a new dissimilarity measure, which integrates the intensity and the geometry shape dissimilarity, is introduced. Because of the presence of the geometrical information, the new measure is able to differentiate the pixels with similar intensity values within different geometrical shape structures. Experimental results show that the new algorithm can get better segmentation.

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Authors and Affiliations

  1. School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

    JuTao Hao

  2. Department of Electrical Engineering, Shanghai University of Electric Power, Shanghai, 200090, China

    JingJing Zhao

  3. Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai, 200240, China

    MingLu Li

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  1. JuTao Hao
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  2. JingJing Zhao
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  3. MingLu Li
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Correspondence to JuTao Hao.

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Hao, J., Zhao, J. & Li, M. Spatial continuity incorporated multi-attribute fuzzy clustering algorithm for blood vessels segmentation. Sci. China Inf. Sci. 53, 752–759 (2010). https://doi.org/10.1007/s11432-010-0072-2

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  • DOI: https://doi.org/10.1007/s11432-010-0072-2

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