Compressed sensing based multi-modal medical image fusion using a combined fusion strategy

Xingbin Liu, Huiqian Du*, Jiadi Bei, Wenbo Mei

*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

In this paper, a novel multi-modality medical image fusion method based on compressed sensing by fusing undersampled k-space data is proposed. In order to transfer structural information from the source images into the fused image clearly, a combined fusion strategy is designed for undersampled low and high frequency subbands of k-space data. The final fused image is reconstructed from fused subband data with the conjugate gradient method. The experimental results demonstrate that the proposed algorithm can substantially reduce sampling data and obtain satisfactory results to meet the demand of clinical diagnosis.

Original languageEnglish
Title of host publicationProceedings - 2015 8th International Conference on BioMedical Engineering and Informatics, BMEI 2015
EditorsZhiyong Tao, Li Bai, Sen Lin, Jinguang Sun, Lipo Wang, Liangshan Shao
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages85-89
Number of pages5
ISBN (Electronic)9781509000227
DOIs
Publication statusPublished - 8 Feb 2016
Event8th International Conference on BioMedical Engineering and Informatics, BMEI 2015 - Shenyang, China
Duration: 14 Oct 201516 Oct 2015

Publication series

NameProceedings - 2015 8th International Conference on BioMedical Engineering and Informatics, BMEI 2015

Conference

Conference8th International Conference on BioMedical Engineering and Informatics, BMEI 2015
Country/TerritoryChina
CityShenyang
Period14/10/1516/10/15

Keywords

  • PCA
  • compressed sensing
  • k-space
  • medical image fusion
  • standard deviation

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