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Multispectral image edge detection via Clifford gradient

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Abstract

The edge detection of a multispectral image with a mass of image information is theoretically expected to give a more accurate edge. Clifford algebra is more suitable for processing multidimensional data and expressing the data association than general algebra. First, the basic properties of Clifford algebra are introduced. Second, the Clifford algebra description of a multispectral image is introduced. Then, a novel multispectral image edge detection algorithm is proposed in terms of the Clifford gradient. In the first two experiments, comparison of the detail achieved by the new algorithm with that from the maximal entropy edge detection algorithm reveals that the edge detection based on Clifford gradient is better at retaining and identifying edge information of the multispectral image than the maximal entropy edge detection algorithm. The experiments using tuberculosis CT image show that the new algorithm has clinical value in reducing risk of misdiagnosis.

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Authors and Affiliations

  1. College of Information Engineering, Shenzhen University, Shenzhen, 518060, China

    Hui Liu & WenMing Cao

  2. Institute of Intelligent Computing Science, Shenzhen University, Shenzhen, 518060, China

    Chen Xu & JiQiang Feng

Authors
  1. Chen Xu
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  2. Hui Liu
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  3. WenMing Cao
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  4. JiQiang Feng
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Correspondence to WenMing Cao.

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Xu, C., Liu, H., Cao, W. et al. Multispectral image edge detection via Clifford gradient. Sci. China Inf. Sci. 55, 260–269 (2012). https://doi.org/10.1007/s11432-011-4540-0

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  • DOI: https://doi.org/10.1007/s11432-011-4540-0

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