Morphological classification: Application to cardiac MRI of tetralogy of fallot

Dong Hye Ye, Harold Litt, Christos Davatzikos, Kilian M. Pohl

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

Abstract

This paper presents an image-based classification method, and applies it to classification of cardiac MRI scans of individuals with Tetralogy of Fallot (TOF). Clinicians frequently diagnose cardiac disease by measuring the ventricular volumes from cardiac MRI scans. Interrater variability is a common issue with these measurements. We address this issue by proposing a fully automatic approach for detecting structural changes in the heart. We first extract morphological features of each subject by registering cardiac MRI scans to a template. We then reduce the size of the features via a nonlinear manifold learning technique. These low dimensional features are then fed into nonlinear support vector machine classifier identifying if the subject of the scan is effected by the disease. We apply our approach to MRI scans of 12 normal controls and 22 TOF patients. Experimental result demonstrates that the method can correctly determine whether subject is normal control or TOF with 91% accuracy.

Original languageEnglish (US)
Title of host publicationFunctional Imaging and Modeling of the Heart - 6th International Conference, FIMH 2011, Proceedings
Pages180-187
Number of pages8
DOIs
StatePublished - 2011
Event6th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2011 - New York City, NY, United States
Duration: May 25 2011May 27 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6666 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other6th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2011
CountryUnited States
CityNew York City, NY
Period5/25/115/27/11

Keywords

  • Computational anatomy
  • Manifold learning
  • Morphological classification
  • Tetralogy of Fallot

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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