Imagen de Portada
Código QR

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

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...

Descripción completa

Publicado en:Medical & Biological Engineering & Computing Vol. 56; no. 9; pp. 1565 - 1579
Autores principales: Liu, Xingbin, Mei, Wenbo, Du, Huiqian
Formato: Journal Article
Publicado: Springer Nature Sep2018
Acceso en línea:Ver este registro en EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=131278133&site=ehost-live
header:
  @attributes:
    shortDbName: ccm
    uiTerm: 131278133
    longDbName: CINAHL Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    dissinfo:
    jinfo:
      jid:
        01400118
        PO0
      jtl: Medical & Biological Engineering & Computing
      issn: 01400118
      maglogo: N
    pubinfo:
      dt: Sep2018
      vid: 56
      iid: 9
      pid: 237
      pub: Springer Nature
      place: New York, New York
    artinfo:
      ui:
        131278133
        131278133
        NLM29435706
        10.1007/s11517-018-1796-1
        NLM29435706
        131278133
      ppf: 1565
      ppct: 14
      formats:
        fmt:
          – @attributes:
              type: T
          – @attributes:
              type: P
      tig:
        atl: Detail-enhanced multimodality medical image fusion based on gradient minimization smoothing filter and shearing filter.
      aug:
        au:
          Liu, Xingbin
          Mei, Wenbo
          Du, Huiqian
        affil: School of Information and Electronics, Beijing Institute of Technology, 100081, Beijing, China
      sug:
        subj:
          Algorithms
          Diagnostic Imaging
          Image Interpretation, Computer Assisted
          Magnetic Resonance Imaging
          Tomography, X-Ray Computed
          Psychological Tests
      ab: 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.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
    refInfo:
    holdings:
      @attributes:
        islocal: N