Feature selection based on iterative canonical correlation analysis for automatic diagnosis of Parkinson’s disease

Luyan Liu, Qian Wang, Ehsan Adeli, Lichi Zhang, Han Zhang, Dinggang Shen

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

12 Citations (Scopus)


Parkinson’s disease (PD) is a major progressive neurodegenerative disorder. Accurate diagnosis of PD is crucial to control the symptoms appropriately. However,its clinical diagnosis mostly relies on the subjective judgment of physicians and the clinical symptoms that often appear late. Recent neuroimaging techniques,along with machine learning methods,provide alternative solutions for PD screening. In this paper,we propose a novel feature selection technique,based on iterative canonical correlation analysis (ICCA),to investigate the roles of different brain regions in PD through T1-weighted MR images. First of all,gray matter and white matter tissue volumes in brain regions of interest are extracted as two feature vectors. Then,a small group of significant features were selected using the iterative structure of our proposed ICCA framework from both feature vectors. Finally,the selected features are used to build a robust classifier for automatic diagnosis of PD. Experimental results show that the proposed feature selection method results in better diagnosis accuracy,compared to the baseline and state-of-the-art methods.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
EditorsGozde Unal, Sebastian Ourselin, Leo Joskowicz, Mert R. Sabuncu, William Wells
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9783319467221
Publication statusPublished - 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9901 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Bibliographical note

Funding Information:
This work is supported by National Natural Science Foundation of China (NSFC) Grants (Nos. 61473190, 61401271, 81471733).

Publisher Copyright:
© Springer International Publishing AG 2016.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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