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Feature selection for shape-based classification of biological objects.

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

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AlgorithmsArtificial IntelligenceComputer SimulationHippocampusHumansImage EnhancementImage Interpretation, Computer-AssistedImaging, Three-DimensionalMagnetic Resonance ImagingModels, BiologicalPattern Recognition, AutomatedReproducibility of ResultsSchizophreniaSensitivity and SpecificitySubtraction TechniqueClinical TrialComparative StudyControlled Clinical TrialJournal ArticleResearch Support, U.S. Gov't, P.H.S.Validation Study

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Paul Yushkevich, Sarang Joshi, Stephen M Pizer, John G Csernansky, Lei E Wang. 2003. Feature selection for shape-based classification of biological objects.. https://doi.org/10.1007/978-3-540-45087-0_10

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