Creating and sharing knowledge for telecommunications

Generative embeddings based on Rician mixtures for kernel-based classification of magnetic resonance images

Carli, A. ; Figueiredo, M. A. T. ; Bicego, M. ; Murino, V.

Neurocomputing Vol. 123, Nº -, pp. 49 - 59, January, 2014.

ISSN (print): 0925-2312
ISSN (online):

Journal Impact Factor: 1,234 (in 2008)

Digital Object Identifier: 10.1016/j.neucom.2013.02.037

Classical approaches to classifier learning for structured objects (such as images or sequences) are based on probabilistic generative models. On the other hand, state-of-the-art classifiers for vectorial data are learned discriminatively. In recent years, these two dual paradigms have been combined via the use of generative embeddings (of which the Fisher kernel is arguably the best known example); these embeddings are mappings from the object space into a fixed dimensional score space, induced by a generative model learned from data, on which a (maybe kernel-based) discriminative approach can then be used. This paper proposes a new semi-parametric approach to build generative embeddings for classification of magnetic resonance images (MRI). Based on the fact that MRI data is well described by Rice distributions, we propose to use Rician mixtures as the underlying generative model, based on which several different generative embeddings are built. These embeddings yield vectorial representations on which kernel-based support vector machines (SVM) can be trained for classification. Concerning the choice of kernel, we adopt the recently proposed nonextensive information theoretic kernels.
The methodology proposed was tested on a challenging classification task, which consists in classifying MRI images as belonging to schizophrenic or non-schizophrenic human subjects. The classification is based on a set of regions of interest (ROIs) in each image, with the classifiers corresponding to each ROI being combined via AdaBoost. The experimental results show that the proposed methodology outperforms the previous state-of-the-art methods on the same dataset.