Landmark-based alzheimer’s disease diagnosis using longitudinal structural MR images

Jun Zhang, Mingxia Liu, Le An, Yaozong Gao, Dinggang Shen

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

Abstract

In this paper, we propose a landmark-based feature extraction method for AD diagnosis using longitudinal structural MR images, which requires no nonlinear registration or tissue segmentation in the application stage and is robust to the inconsistency among longitudinal scans. Specifically, (1) the discriminative landmarks are first automatically discovered from the whole brain, which can be efficiently localized using a fast landmark detection method for the testing images; (2) Highlevel statistical spatial features and contextual longitudinal features are then extracted based on those detected landmarks. Using the spatial and longitudinal features, a linear support vector machine (SVM) is adopted for distinguishing AD subjects from healthy controls (HCs) and also mild cognitive impairment (MCI) subjects from HCs, respectively. Experimental results demonstrate the competitive classification accuracies, as well as a promising computational efficiency.

Original languageEnglish
Title of host publicationMedical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging - MICCAI 2016 International Workshops, MCV and BAMBI, Revised Selected Papers
PublisherSpringer Verlag
Pages35-45
Number of pages11
Volume10081 LNCS
ISBN (Print)9783319611877
DOIs
Publication statusPublished - 2017
EventInternational Workshop on Medical Computer Vision, MCV 2016, and of the International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2016, held in conjunction with the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece
Duration: 2016 Oct 212016 Oct 21

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10081 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherInternational Workshop on Medical Computer Vision, MCV 2016, and of the International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2016, held in conjunction with the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
CountryGreece
CityAthens
Period16/10/2116/10/21

Fingerprint

Alzheimer's Disease
Landmarks
Computational efficiency
Support vector machines
Feature extraction
Brain
Tissue
Testing
Inconsistency
Computational Efficiency
Registration
Feature Extraction
Support Vector Machine
Segmentation
Experimental Results
Demonstrate

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Zhang, J., Liu, M., An, L., Gao, Y., & Shen, D. (2017). Landmark-based alzheimer’s disease diagnosis using longitudinal structural MR images. In Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging - MICCAI 2016 International Workshops, MCV and BAMBI, Revised Selected Papers (Vol. 10081 LNCS, pp. 35-45). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10081 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-61188-4_4

Landmark-based alzheimer’s disease diagnosis using longitudinal structural MR images. / Zhang, Jun; Liu, Mingxia; An, Le; Gao, Yaozong; Shen, Dinggang.

Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging - MICCAI 2016 International Workshops, MCV and BAMBI, Revised Selected Papers. Vol. 10081 LNCS Springer Verlag, 2017. p. 35-45 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10081 LNCS).

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

Zhang, J, Liu, M, An, L, Gao, Y & Shen, D 2017, Landmark-based alzheimer’s disease diagnosis using longitudinal structural MR images. in Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging - MICCAI 2016 International Workshops, MCV and BAMBI, Revised Selected Papers. vol. 10081 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10081 LNCS, Springer Verlag, pp. 35-45, International Workshop on Medical Computer Vision, MCV 2016, and of the International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2016, held in conjunction with the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, Athens, Greece, 16/10/21. https://doi.org/10.1007/978-3-319-61188-4_4
Zhang J, Liu M, An L, Gao Y, Shen D. Landmark-based alzheimer’s disease diagnosis using longitudinal structural MR images. In Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging - MICCAI 2016 International Workshops, MCV and BAMBI, Revised Selected Papers. Vol. 10081 LNCS. Springer Verlag. 2017. p. 35-45. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-61188-4_4
Zhang, Jun ; Liu, Mingxia ; An, Le ; Gao, Yaozong ; Shen, Dinggang. / Landmark-based alzheimer’s disease diagnosis using longitudinal structural MR images. Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging - MICCAI 2016 International Workshops, MCV and BAMBI, Revised Selected Papers. Vol. 10081 LNCS Springer Verlag, 2017. pp. 35-45 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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