Multimodal medical image fusion by combining gradient minimization smoothing filter and non-subsampled directional filter bank

Cheng Zhang, Wenbo Mei, Huiqian Du*, Zexian Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

A new algorithm was proposed for medical images fusion in this paper, which combined gradient minimization smoothing filter (GMSF) with non-sampled directional filter bank (NSDFB). In order to preserve more detail information, a multi scale edge preserving decomposition framework (MEDF) was used to decompose an image into a base image and a series of detail images. For the fusion of base images, the local Gaussian membership function is applied to construct the fusion weighted factor. For the fusion of detail images, NSDFB was applied to decompose each detail image into multiple directional sub-images that are fused by pulse coupled neural network (PCNN) respectively. The experimental results demonstrate that the proposed algorithm is superior to the compared algorithms in both visual effect and objective assessment.

Original languageEnglish
Title of host publicationNinth International Conference on Graphic and Image Processing, ICGIP 2017
EditorsHui Yu, Junyu Dong
PublisherSPIE
ISBN (Electronic)9781510617414
DOIs
Publication statusPublished - 2018
Event9th International Conference on Graphic and Image Processing, ICGIP 2017 - Qingdao, China
Duration: 14 Oct 201716 Oct 2017

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10615
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference9th International Conference on Graphic and Image Processing, ICGIP 2017
Country/TerritoryChina
CityQingdao
Period14/10/1716/10/17

Keywords

  • Image fusion
  • gradient minimization smoothing filter
  • non sub-sampled directional filter bank
  • pulse coupled neural network

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