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.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Vemuri, P., Gunter, J., et al.: Alzheimer’s disease diagnosis in individual subjects using structural mr images: Validation studies. Neuroimage 39, 1186–1197 (2008)
Wang, L.: Feature selection with kernel class separability. IEEE Trans. Pattern Analysis and Machine Intelligence 30(9), 1534–1546 (2008)
Zhou, L., Wang, L., Shen, C.: Feature selection with redundancy-constrained class separability. IEEE Trans. Neural. Networks 21(5), 853–858 (2010)
Dinkelbach, W.: On nonlinear fractional programming. Management Science 13(7) (1967)
Schrijver, A.: Theory of Linear and Integer Programming. John Wiley and Sons, Chichester (1986)
Csernansky, J., Wang, L., Swank, J., Miller, J., Gado, M., McKeel, D., Miller, M., Morris, J.: Preclinical detection of Alzheimer’s disease: hippocampal shape and volume predict dementia onset in the elderly. Neuroimage 25, 783–792 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhou, L., Wang, L., Shen, C., Barnes, N. (2010). Hippocampal Shape Classification Using Redundancy Constrained Feature Selection. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010. MICCAI 2010. Lecture Notes in Computer Science, vol 6362. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15745-5_33
Download citation
DOI: https://doi.org/10.1007/978-3-642-15745-5_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15744-8
Online ISBN: 978-3-642-15745-5
eBook Packages: Computer ScienceComputer Science (R0)