In this paper we exploit the Functional maps approach for brain classification. The functional representation of brain shapes, or their subparts, enables us to improve the detection of morphological abnormalities associated with the analyzed disease. The proposed method is based on the spectral shape paradigm that is largely used for generic geometric processing but still few exploited in the medical context. The key aspect of the Functional maps framework is that it moves the estimation of correspondences from the shape space to the functional space enhancing the potential of spectral analysis. Moreover, we propose a new kernel, called the Functional maps kernel (FM-kernel) for the Support Vector Machine (SVM) classification that is specifically designed to work on the functional space. The obtained results for bipolar disorder detection on the putamen regions are promising in comparison with other spectral-based approaches.

Functional maps for brain classification on spectral domain / S. Melzi, A. Mella, L. Squarcina, M. Bellani, C. Perlini, M. Ruggeri, C.A. Altamura, P. Brambilla, U. Castellani. ((Intervento presentato al convegno 1th International Workshop on Spectral and Shape Analysis in Medical Imaging- SeSAMI Held in Conjunction with 19th International Conference on Medical Image Computing and Computer Assisted Interventions, MICCAI tenutosi a Athens nel 2016.

Functional maps for brain classification on spectral domain

L. Squarcina;P. Brambilla;
2016

Abstract

In this paper we exploit the Functional maps approach for brain classification. The functional representation of brain shapes, or their subparts, enables us to improve the detection of morphological abnormalities associated with the analyzed disease. The proposed method is based on the spectral shape paradigm that is largely used for generic geometric processing but still few exploited in the medical context. The key aspect of the Functional maps framework is that it moves the estimation of correspondences from the shape space to the functional space enhancing the potential of spectral analysis. Moreover, we propose a new kernel, called the Functional maps kernel (FM-kernel) for the Support Vector Machine (SVM) classification that is specifically designed to work on the functional space. The obtained results for bipolar disorder detection on the putamen regions are promising in comparison with other spectral-based approaches.
Brain classification; Diseases and disorders detection; Functional maps; Spectral shape analysis
Settore INF/01 - Informatica
Harvard Medical School, Boston
Inria Sophia-Antipolis, France
Ludwig Maximilian University, Munich
Functional maps for brain classification on spectral domain / S. Melzi, A. Mella, L. Squarcina, M. Bellani, C. Perlini, M. Ruggeri, C.A. Altamura, P. Brambilla, U. Castellani. ((Intervento presentato al convegno 1th International Workshop on Spectral and Shape Analysis in Medical Imaging- SeSAMI Held in Conjunction with 19th International Conference on Medical Image Computing and Computer Assisted Interventions, MICCAI tenutosi a Athens nel 2016.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/786653
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