Please use this identifier to cite or link to this item:
Scopus Web of Science® Altmetric
Type: Conference paper
Title: Hippocampal shape classification using redundancy constrained feature selection
Author: Zhou, L.
Wang, L.
Shen, C.
Barnes, N.
Citation: Proceedings of 13th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'10), 20-24th September, 2010 / T. Jiang, N. Navab, J. P.W. Pluim and M. A. Viergever (eds.); Part II pp.266-273
Publisher: Springer
Publisher Place: New York
Issue Date: 2010
Series/Report no.: Lecture Notes in Computer Science; Vol. 6362
ISBN: 3642157440
ISSN: 0302-9743
Conference Name: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) (13th : 2011 : Beijing, China)
Statement of
Luping Zhou, Lei Wang, Chunhua Shen and Nick Barnes
Abstract: Landmark-based 3D hippocampal shape classification involves high-dimensional descriptor space, many noisy and redundant features, and a very small number of training samples. Feature selection becomes critical in this situation, because it not only improves classification performance, but also identifies the regions that contribute more to shape discrimination. This work identifies the drawbacks of SVM-RFE, and proposes a novel class-separability-based feature selection approach to overcome them. We formulate feature selection as a constrained integer optimization and develop a new algorithm to efficiently and optimally solve this problem. Theoretical analysis and experimental study on both synthetic data and real hippocampus data demonstrate its superior performance over the prevailing SVM-RFE. Our work provides a new efficient feature selection tool for hippocampal shape classification.
Keywords: Hippocampus
Alzheimer Disease
Image Interpretation, Computer-Assisted
Imaging, Three-Dimensional
Magnetic Resonance Imaging
Image Enhancement
Sensitivity and Specificity
Reproducibility of Results
Pattern Recognition, Automated
Rights: © Springer, Part of Springer Science+Business Media
DOI: 10.1007/978-3-642-15745-5_33
Appears in Collections:Aurora harvest 5
Computer Science publications

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.