Feature selection for shape-based classification of biological objects

Paul Yushkevich*, Sarang Joshi, Stephen M. Pizer, John G. Csernansky, Lei E. Wang

*Corresponding author for this work

Research output: Contribution to journalArticle

27 Scopus citations

Abstract

This paper introduces a method for selecting subsets of relevant statistical features in biological shape-based classification problems. The method builds upon existing feature selection methodology by introducing a heuristic that favors the geometric locality of the selected features. This heuristic effectively reduces the combinatorial search space of the feature selection problem. The new method is tested on synthetic data and on clinical data from a study of hippocampal shape in schizophrenia. Results on clinical data indicate that features describing the head of the right hippocampus are most relevant for discrimination.

Original languageEnglish (US)
Pages (from-to)114-125
Number of pages12
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2732
StatePublished - Dec 1 2003

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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