Detail-enhanced multimodality medical image fusion based on gradient minimization smoothing filter and shearing filter.

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  • Author(s): Liu X;Liu X; Mei W; Mei W; Du H; Du H
  • Source:
    Medical & biological engineering & computing [Med Biol Eng Comput] 2018 Sep; Vol. 56 (9), pp. 1565-1578. Date of Electronic Publication: 2018 Feb 13.
  • Publication Type:
    Journal Article
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: Springer Country of Publication: United States NLM ID: 7704869 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1741-0444 (Electronic) Linking ISSN: 01400118 NLM ISO Abbreviation: Med Biol Eng Comput Subsets: MEDLINE
    • Publication Information:
      Publication: New York, NY : Springer
      Original Publication: Stevenage, Eng., Peregrinus.
    • Subject Terms:
      Algorithms* ; Image Interpretation, Computer-Assisted* ; Multimodal Imaging*; Humans ; Magnetic Resonance Imaging ; Tomography, X-Ray Computed
    • Abstract:
      In this paper, a detail-enhanced multimodality medical image fusion algorithm is proposed by using proposed multi-scale joint decomposition framework (MJDF) and shearing filter (SF). The MJDF constructed with gradient minimization smoothing filter (GMSF) and Gaussian low-pass filter (GLF) is used to decompose source images into low-pass layers, edge layers, and detail layers at multiple scales. In order to highlight the detail information in the fused image, the edge layer and the detail layer in each scale are weighted combined into a detail-enhanced layer. As directional filter is effective in capturing salient information, so SF is applied to the detail-enhanced layer to extract geometrical features and obtain directional coefficients. Visual saliency map-based fusion rule is designed for fusing low-pass layers, and the sum of standard deviation is used as activity level measurement for directional coefficients fusion. The final fusion result is obtained by synthesizing the fused low-pass layers and directional coefficients. Experimental results show that the proposed method with shift-invariance, directional selectivity, and detail-enhanced property is efficient in preserving and enhancing detail information of multimodality medical images. Graphical abstract The detailed implementation of the proposed medical image fusion algorithm.
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    • Contributed Indexing:
      Keywords: Edge-preserving filter; Gaussian smoothing filter; Medical image fusion; Multi-scale decomposition; Non-subsampled shearlet transform
    • Publication Date:
      Date Created: 20180214 Date Completed: 20181112 Latest Revision: 20181113
    • Publication Date:
      20240628
    • Accession Number:
      10.1007/s11517-018-1796-1
    • Accession Number:
      29435706