Paper Info
Title
Morphometric Analysis of Brain Structures for Improved Discrimination
Abstract
We perform discriminative analysis of brain structures using morphometric information. Spherical harmonics technique and point distribution model are used for shape description. Classification is performed using linear discriminants and support vector machines with several feature selection approaches. We consider both inclusion and exclusion of volume information in the discrimination. We perform extensive experimental studies by applying different combinations of techniques to hippocampal data in schizophrenia and achieve best jackknife classification accuracies of 95% (whole set) and 90% (right-banded males), respectively. Our results find that the left hippocampus is a better predictor than the right in the complete dataset, but that the right hippocampus is a stronger predictor than the left in the right-handed male subset. We also propose a new method for visualization of discriminative patterns.
Year
DOI
Venue
2003
10.1007/978-3-540-39903-2_63
Lecture Notes in Computer Science
Keywords
Field
DocType
point distribution model,feature selection,spherical harmonic,support vector machine,discriminant analysis
Point distribution model,Jackknife resampling,Bayesian inference,Feature selection,Pattern recognition,Visualization,Computer science,Support vector machine,Artificial intelligence,Discriminative model,Machine learning,Right hippocampus
Conference
Volume
ISSN
Citations 
2879
0302-9743
5
PageRank 
References 
Authors
0.55
8
7
Name
Order
Citations
PageRank
Li Shen1863102.99
James Ford2597.31
Fillia Makedon31676201.73
Yuhang Wang415916.49
Tilmann Steinberg5143.10
Song Ye6484.16
Saykin Andrew J763166.57