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Multimodal Medical Image Fusion in Extended Contourlet Transform Domain

  • Seiichi Serikawa
  • Huimin Lu
  • Yujie Li
  • Lifeng Zhang
  • Shiyuan Yang
  • Akira Yamawaki
  • Shota Nakashima
  • Yuhki Kitazono
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 443)

Abstract

As a novel of multi-resolution analysis tool, the modified sharp frequency localized contourlet transforms (MSFLCT) provides flexible multiresolution, anisotropy, and directional expansion for medical images. In this paper, we proposed a new fusion rule for multimodal medical images based on MSFLCT. The multimodal medical images are decomposed by MSFLCT. For the high-pass subband, the weighted sum modified laplacian (WSML) method is used for choose the high frequency coefficients. For the lowpass subband, the maximum local energy (MLE) method is combined with “region” idea for low frequency coefficient selection. The final fusion image is obtained by applying inverse MSFLCT to fused lowpass and highpass subbands. Abundant experiments have been made on groups of multimodality datasets, both human visual and quantitative analysis show that the new strategy for attaining image fusion with satisfactory performance.

Keywords

multimodal medical image fusion maximum local energy contourlet transform 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Seiichi Serikawa
    • 1
  • Huimin Lu
    • 1
  • Yujie Li
    • 1
  • Lifeng Zhang
    • 1
  • Shiyuan Yang
    • 1
  • Akira Yamawaki
    • 1
  • Shota Nakashima
    • 2
  • Yuhki Kitazono
    • 3
  1. 1.Department of Electrical Engineering and ElectronicsKyushu Institute of TechnologyKitakyushuJapan
  2. 2.Department of Electrical EngineeringUbe National College of TechnologyUbeJapan
  3. 3.Department of Electrical EngineeringKitakyushu National College of TechnologyKitakyushuJapan

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