Nonlinear probabilistic latent variable models for groupwise correspondence analysis in brain structures

Hernan F. Garcia, Alvaro A. Orozco, Mauricio A. Alvarez

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

Abstract

Neuroimage correspondence analysis is critical in applications that model neurodegenerative disease progression. Establishing meaningful relations between non-rigid objects such as brain structures poses a challenging topic in the bio-imaging signal processing field. In this paper, we introduce a novel nonlinear probabilistic latent variable model approach to infer shape correspondences of brain structures. To this end, we perform an unsupervised clustering process that is automatically carried out by a nonlinear kernelized probabilistic latent variable model. The kernel embeddings are accomplished by using random Fourier features as nonlinear mappings of 3D shape descriptors. We experimentally show how the model proposed can accurately establish meaningful relations between any pair of non-rigid shapes such as those brain structures related to the Alzheimer's disease.

Original languageEnglish
Title of host publication2018 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Proceedings
EditorsNelly Pustelnik, Zheng-Hua Tan, Zhanyu Ma, Jan Larsen
PublisherIEEE Computer Society
ISBN (Electronic)9781538654774
DOIs
Publication statusPublished - 31 Oct 2018
Event28th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Aalborg, Denmark
Duration: 17 Sept 201820 Sept 2018

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume2018-September
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Conference

Conference28th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018
Country/TerritoryDenmark
CityAalborg
Period17/09/1820/09/18

Keywords

  • Correspondence problem
  • Neuroimage analysis
  • Probabilistic latent variable models
  • Random Fourier features

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