Abstract
This paper introduces a continuous framework to spatially regularize support vector machines (SVM) for brain image analysis based on the Fisher metric. We show that, by considering the images as elements of a statistical manifold, one can define a metric that integrates various types of information. Based on this metric, replacing the standard SVM regularization with a Laplace-Beltrami regularization operator allows integrating to the classifier various types of constraints based on spatial and anatomical information. The proposed framework is applied to the classification of magnetic resonance (MR) images based on gray matter concentration maps from 137 patients with Alzheimer’s disease and 162 elderly controls. The results demonstrate that the proposed classifier generates less-noisy and consequently more interpretable feature maps with no loss of classification performance.
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Cuingnet, R., Glaunès, J.A., Chupin, M., Benali, H., Colliot, O. (2011). Anatomical Regularization on Statistical Manifolds for the Classification of Patients with Alzheimer’s Disease. In: Suzuki, K., Wang, F., Shen, D., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2011. Lecture Notes in Computer Science, vol 7009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24319-6_25
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DOI: https://doi.org/10.1007/978-3-642-24319-6_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24318-9
Online ISBN: 978-3-642-24319-6
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