Abstract
In this paper a novel framework for brain classification is proposed in the context of mental health research. A learning by example method is introduced by combining local measurements with non linear Support Vector Machine. Instead of considering a voxel-by-voxel comparison between patients and controls, we focus on landmark points which are characterized by local region descriptors, namely Scale Invariance Feature Transform (SIFT). Then, matching is obtained by introducing the local kernel for which the samples are represented by unordered set of features. Moreover, a new weighting approach is proposed to take into account the discriminative relevance of the detected groups of features. Experiments have been performed including a set of 54 patients with schizophrenia and 54 normal controls on which region of interest (ROI) have been manually traced by experts. Preliminary results on Dorso-lateral PreFrontal Cortex (DLPFC) region are promising since up to 75% of successful classification rate has been obtained with this technique and the performance has improved up to 85% when the subjects have been stratified by sex.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Giuliania, N., Calhon, V., Pearlson, V., Francisd, A., Buchanan, R.: Voxel-based morphometry versus region of interest: a comparison of two methods for analyzing gray matter differences in schizophrenia. Schizophrenia Research 74, 135–147 (2005)
Baiano, M., Perlini, C., Rambaldelli, G., Cerini, R., Dusi, N., Bellani, M., Spezzapria, G., Versace, A., Balestieri, M., Mucelli, R.P., Tansella, M., Brambilla, P.: Decreased entorhinal cortex volumes in schizophrenia. Schizophrenia Research 102, 171–180 (2008)
Ashburner, J., Friston, K.: Voxel-based morphometry - the methods. Neuroimage 11, 805–821 (2000)
Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. John Wiley and Sons, Chichester (2001)
Gerig, G., Styner, M.A., Shenton, M.E., Lieberman, J.A.: Shape versus size: Improved understanding of the morphology of brain structures. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, p. 24. Springer, Heidelberg (2001)
Fan, Y., Shen, D., Gur, R., Gur, R., Davatzikos, C.: Compare: classification of morphological patterns using adaptive regional elements. IEEE Transaction on Medical Imaging 26(1), 93–105 (2007)
Yoon, U., Lee, J., Im, K., Shin, W., Cho, B.H., Kim, I., Kwon, J., Kim, S.: Pattern classification using principal components of cortical thickness and its discriminative pattern in schizophrenia. Neuroimage 34, 1405–1415 (2007)
Potkin, S.G., et al.: Working memory and dlpfc inefficiency in schizophrenia: The fbirn study. Schizophrenia Bulletin 35(1), 19–31 (2009)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Urschler, M., Bauer, J., Ditt, H., Bischof, H.: SIFT and Shape Context for feature-based nonlinear registration of thoracic CT images. Computer Vision Approach to Medical Image Analysis, 73–84 (2006)
Cruska, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: ECCV Workshop on Statistical Learning in Computer Vision, pp. 1–22 (2004)
Grauman, K., Darrell, T.: The pyramid match kernel: Efficient learning with sets of features. Journal of Machine Learning Research 8(2), 725–760 (2007)
Burges, C.: A tutorial on support vector machine for pattern recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)
Andreone, N., Tansella, M., Cerini, R., Versace, A., Rambaldelli, G., Perlini, C., Dusi, N., Pelizza, L., Balestrieri, M., Barbui, C., Nosé, M., Gasparini, A., Brambilla, P.: Cortical white-matter microstructure in schizophrenia. diffusion imaging study. Br. J. Psychiatry 191(8), 113–119 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Castellani, U. et al. (2009). Local Kernel for Brains Classification in Schizophrenia. In: Serra, R., Cucchiara, R. (eds) AI*IA 2009: Emergent Perspectives in Artificial Intelligence. AI*IA 2009. Lecture Notes in Computer Science(), vol 5883. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10291-2_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-10291-2_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10290-5
Online ISBN: 978-3-642-10291-2
eBook Packages: Computer ScienceComputer Science (R0)