Co-neighbor multi-view spectral embedding for medical content-based retrieval

Hangyu Che, Sidong Liu, Weidong Cai, Sonia Pujol, Ron Kikinis, Dagan Feng

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

9 Citations (Scopus)

Abstract

Multimodal medical data from various information sources are often used to depict patients. We refer to each source as a 'view'. Multi-view features could provide complementary information to each other; thus by fusing the multi-view features, we could greatly enhance the current medical content-based retrieval framework. In this paper, we propose a Co-neighbor Multi-view Spectral Embedding (CMSE) algorithm, which is an advanced feature fusion method based on the multi-view spectral analysis. CMSE aims to seek a smooth embedding for the multi-view features by maximizing the neighborhood affinity across all feature spaces. We evaluated the proposed CMSE algorithm using a freely available neuroimaging database, ADNI, with 331 pre-diagnosed subjects. Totally, 9 views of features were extracted for validation, and an improved retrieval performance was achieved over other state-of-art feature fusion methods.

Original languageEnglish
Title of host publication2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages911-914
Number of pages4
ISBN (Electronic)9781467319591, 9781467319614
DOIs
Publication statusPublished - 1 Jan 2014
Externally publishedYes
Event2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 - Beijing, China
Duration: 29 Apr 20142 May 2014

Conference

Conference2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
CountryChina
CityBeijing
Period29/04/142/05/14

Keywords

  • Dimensionality reduction
  • Multiple views
  • Neuroimaging
  • Spectral embedding

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  • Cite this

    Che, H., Liu, S., Cai, W., Pujol, S., Kikinis, R., & Feng, D. (2014). Co-neighbor multi-view spectral embedding for medical content-based retrieval. In 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 (pp. 911-914). [6868019] Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ISBI.2014.6868019