Abstract
We construct a computational framework for automatic central nervous system (CNS) disease discrimination using high resolution Magnetic Resonance Images (MRI) of human brains. More than 3000 MR image features are extracted, forming a high dimensional coarse-to-fine hierarchical image description that quantifies brain asymmetry, texture and statistical properties in corresponding local regions of the brain. Discriminative image feature subspaces are computed, evaluated and selected automatically. Our initial experimental results show 100% and 90% separability between chronicle schizophrenia (SZ) and first episode SZ versus their respective matched controls. Under the same computational framework, we also find higher than 95% separability among Alzheimer’s Disease, mild cognitive impairment patients, and their matched controls. An average of 88% classification success rate is achieved using leave-one-out cross validation on five different well-chosen patient-control image sets of sizes from 15 to 27 subjects per disease class.
This work is supported in part by NIH grants AG05133 and DA015900-01.
Chapter PDF
Similar content being viewed by others
Keywords
- Mild Cognitive Impairment Patient
- Magnetic Resonance Image Feature
- Feature Subset Selection
- High Resolution Magnetic Resonance Image
- Discriminative Subspace
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Bilder, R., Wu, H., Bogerts, B., Ashtari, M., Robinson, D., Woerner, M., Lieberman, J., Degreef, G.: Cerebral volume asymmetries in schizophrenia and mood disorders: a quantitative magnetic resonance imaging study. International Journal of Psychophysiology 34(3), 197–205 (1999)
Bishop, C.M.: Neural Networks for Pattern Recognition. Clarendon Press, Oxford (1995) ISBN:0198538499
Buchanan, R., Vladar, K., Barta, P., Pearlson, G.: Structural evaluation of the prefrontal cortex in schizophrenia. Am. J. of Psychiatry 155, 1049–1055 (1998)
Collins, D.L., Zijdenbos, A., Kollokian, V., Sled, J.G., Kabani, N.J., Holmes, C.J., Evans, A.C.: Design and construction of a realistic digital brain phantom. IEEE Trans. Med. Imag. 17, 463–468 (1998)
Crow, T.: Schizophrenia as an anomaly of cerebral asymmetry. In: Maurer, K. (ed.) Imaging of the brian in psychiatry and related fields, Springer, Heidelberg (1993)
Duda, R., Hart, P., Stork, D.: Pattern Classification. John Wiley & Sons, New York (2001)
Freeborough, P., Fox, N.C.: MR image texture analysis applied to the diagnosis and tracking of alzheimer’s disease. IEEE Transactions on Medical Imaging 17(3), 475–479 (1998)
Jain, A., Duin, R., Mao, J.: Statistical pattern recognition: a review. IEEE Trans. Pattern Analysis and Machine Intelligence 22(1), 4–37 (2000)
Kikinis, R., Shenton, M., Iosifescu, D., McCarley, R., Saiviroonporn, P., Hokama, H., Robatino, A., Metcalf, D., Wible, C., Portas, C., Donnino, R., Jolesz, F.: A digital brain atlas for surgical planning, model-driven segmentation, and teaching. IEEE Transactions on visualization and computer graphics 2(3), 232–240 (1996)
Law, K.: Textured Image Segmentation. PhD thesis, University of Southern California (January 1980)
Liu, Y., Collins, R., Rothfus, W.: Robust Midsagittal Plane Extraction from Normal and Pathological 3D Neuroradiology Images. IEEE Transactions on Medical Imaging 20(3), 175–192 (2001)
Liu, Y., Teverovskiy, L., Carmichael, O., Kikinis, R., Shenton, M., Carter, C., Stenger, V., Davis, S., Aizenstein, H., Becker, J., Lopez, O., Meltzer, M.: Discriminative mr image feature analysis for automatic schizophrenia and alzheimer’s disease classification. Technical Report CMU-RI-TR-04-15, The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA (2004)
Maes, F., Collignon, A., Vandermeulun, D., Marchal, G., Suetens, P.: Multimodality image registration by maximization of mutual information. IEEE Transactions on Medical Imaging 16(2), 187–198 (1997)
McCarley, R., Wible, C., Frumin, Y., Levitt, J., Fischer, I., Shenton, M.: MRI anatomy of schizophrenia. Society of Biological Psychiatry 45, 1099–1119 (1999)
Rex, D., Ma, J., Toga, A.: The LONI pipeline processing environment. Neuroimage 19(3), 1033–1048 (2003)
Shenton, M., Gerig, G., McCarley, R., Szekely, G., Kikinis, R.: Amygdalahippocampal shape differences in schizophrenia: the application of 3d shape models to volumetric mr data. Psychiatry Research Neuroimaging 115, 15–35 (2002)
Shenton, M., Kikinis, R., Jolesz, F., Pollak, S., Lemay, M., Wible, C., Hokama, H., Martin, J., Metcalf, B., Coleman, M., Robert, M.A., McCarley, T.: Abnormalities of the left temporal lobe in schizophrenia response to roth, pfefferbaum and to klimke and knecht. New England Journal of Medicine 327, 75–84 (1992)
Thompson, P., MacDonald, D., Mega, M., Holmes, C., Evans, A., Toga, A.: Detection and mapping of abnormal brain structure with a probabilistic atlas of cortical surfaces. Journal of Computer Assisted Tomography 21(4), 567–581 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, Y. et al. (2004). Discriminative MR Image Feature Analysis for Automatic Schizophrenia and Alzheimer’s Disease Classification. In: Barillot, C., Haynor, D.R., Hellier, P. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004. MICCAI 2004. Lecture Notes in Computer Science, vol 3216. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30135-6_48
Download citation
DOI: https://doi.org/10.1007/978-3-540-30135-6_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22976-6
Online ISBN: 978-3-540-30135-6
eBook Packages: Springer Book Archive