Spatial and anatomical regularization of SVM for brain image analysis

Abstract : Support vector machines (SVM) are increasingly used in brain image analyses since they allow capturing complex multivariate relationships in the data. Moreover , when the kernel is linear, SVMs can be used to localize spatial patterns of discrimination between two groups of subjects. However, the features' spatial distribution is not taken into account. As a consequence, the optimal margin hyperplane is often scattered and lacks spatial coherence, making its anatomical interpretation difficult. This paper introduces a framework to spatially regularize SVM for brain image analysis. We show that Laplacian regularization provides a flexible framework to integrate various types of constraints and can be applied to both cortical surfaces and 3D brain images. The proposed framework is applied to the classification of MR images based on gray matter concentration maps and cortical thickness measures from 30 patients with Alzheimer's disease and 30 elderly controls. The results demonstrate that the proposed method enables natural spatial and anatomical regularization of the classifier.
Complete list of metadatas

Cited literature [30 references]  Display  Hide  Download

https://hal.inria.fr/hal-01439123
Contributor : Olivier Colliot <>
Submitted on : Wednesday, January 18, 2017 - 12:15:30 PM
Last modification on : Friday, March 22, 2019 - 1:44:25 AM
Long-term archiving on : Wednesday, April 19, 2017 - 1:53:31 PM

File

nips2010-camera_ready.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01439123, version 1

Citation

Rémi Cuingnet, Marie Chupin, Habib Benali, Olivier Colliot. Spatial and anatomical regularization of SVM for brain image analysis. Neural Information Processing Systems NIPS 2010, 2010, Vancouver, Canada. ⟨hal-01439123⟩

Share

Metrics

Record views

319

Files downloads

45