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Fusion of Multimodal Color Medical Images Using Quaternion Principal Component Analysis

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Data Science (ICPCSEE 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 728))

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Abstract

Multimodal medical image fusion is used to merge functional and structural information of the same body organ. Most of the multimodal image fusion algorithms are designed to fuse grayscale images that are produced by different imaging modalities. It is likely that problems of colour distortion and information loss will occur in fused image when source images are fused by using algorithms that are not originally designed to fuse colour images. These problems can be avoided by representing and processing source images as quaternion numbers. Quaternion representation of a colour pixel encodes information of its colour channels on the imaginary parts of a quaternion number and provides the advantage to processing colour information holistically as a vector field. In this paper, we proposed an image fusion algorithm based on Quaternion Principal Component Analysis (QPCA), to fuse multimodal colour medical images. Quaternion principal components are calculated by decomposing quaternion covariance matrix using Quaternion Eigenvalue Decomposition (QEVD). Fusion rule is designed, based on the fusion weights that are extracted from the highly influential principal component. To test the performance of the proposed algorithm, experiments have been performed on six image-sets of multimodal colour images of the brain. Experimental results are compared objectively with existing image fusion algorithms. Comparison results show that the proposed algorithm performed better than existing algorithms in fusing colour medical images.

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Acknowledgements

This work was partially supported by Natural Science Foundation of China (No. U1401252, 61472055, 61572092), Program for New Century Excellent Talents in University of China (NCET-11-1085) and Chongqing outstanding Youth Fund (cstc2014jcyjjq40001).

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Authors and Affiliations

  1. Chongqing University of Posts and Telecommunications, Chongqing, China

    Qamar Nawaz, Xiao Bin, Li Weisheng & Isma Hamid

Authors
  1. Qamar Nawaz
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  2. Xiao Bin
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  3. Li Weisheng
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  4. Isma Hamid
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Corresponding author

Correspondence to Qamar Nawaz .

Editor information

Editors and Affiliations

  1. Central South University, Changsha, China

    Beiji Zou

  2. Harbin Engineering University, Harbin, China

    Qilong Han

  3. Harbin University of Science and Technology, Harbin, China

    Guanglu Sun

  4. Northeast Forestry University, Harbin, China

    Weipeng Jing

  5. Huaihua University, Huaihua, Hunan, China

    Xiaoning Peng

  6. Sciences of Country Tripod Institute of Data Science, Harbin, China

    Zeguang Lu

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© 2017 Springer Nature Singapore Pte Ltd.

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Nawaz, Q., Bin, X., Weisheng, L., Hamid, I. (2017). Fusion of Multimodal Color Medical Images Using Quaternion Principal Component Analysis. In: Zou, B., Han, Q., Sun, G., Jing, W., Peng, X., Lu, Z. (eds) Data Science. ICPCSEE 2017. Communications in Computer and Information Science, vol 728. Springer, Singapore. https://doi.org/10.1007/978-981-10-6388-6_33

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  • DOI: https://doi.org/10.1007/978-981-10-6388-6_33

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6387-9

  • Online ISBN: 978-981-10-6388-6

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