, Volume 11, Issue 2, pp 243-258
Date: 21 Sep 2006

SVM feature selection for classification of SPECT images of Alzheimer's disease using spatial information

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Alzheimer's disease is the most frequent type of dementia for elderly patients. Due to aging populations, the occurrence of this disease will increase in the next years. Early diagnosis is crucial to be able to develop more powerful treatments. Brain perfusion changes can be a marker for Alzheimer's disease. In this article, we study the use of SPECT perfusion imaging for the diagnosis of Alzheimer's disease differentiating between images from healthy subjects and images from Alzheimer's disease patients. Our classification approach is based on a linear programming formulation similar to the 1-norm support vector machines. In contrast with other linear hyperplane-based methods that perform simultaneous feature selection and classification, our proposed formulation incorporates proximity information about the features and generates a classifier that does not just select the most relevant voxels but the most relevant “areas” for classification resulting in more robust classifiers that are better suitable for interpretation. This approach is compared with the classical Fisher linear discriminant (FLD) classifier as well as with statistical parametric mapping (SPM). We tested our method on data from four European institutions. Our method achieved sensitivity of 84.4% at 90.9% specificity, this is considerable better the human experts. Our method also outperformed the FLD and SPM techniques. We conclude that our approach has the potential to be a useful help for clinicians.

Glenn Fung received a B.S. degree in pure mathematics from Universidad Lisandro Alvarado in Barquisimeto, Venezuela, then earned an M.S. in applied mathematics from Universidad Simon Bolivar, Caracas, Venezuela, where later he worked as an assistant professor for 2 years. Later, he earned an M.S. degree and a Ph.D. degree in computer sciences from the University of Wisconsin-Madison. His main interests are optimization approaches to machine learning and data mining, with emphasis in support vector machines. In the summer of 2003, he joined the computer aided diagnosis group at Siemens, Medical Solutions in Malvern, PA, where he has been applying machine learning techniques to solve challenging problems that arise in the medical domain. His recent papers are available at www.cs.wisc.edu/gfung.
Jonathan Stoeckel received a B.E. degree from Xi’an Jiao Tong University, Xi'an, China, in 1993 and an M.E. degree from Shanghai Jiao Tong University, Shanghai, China, in 1996. From 1997 to 1998, he did research work in the Data Mining Group at the School of Computing and Information Technology, Griffith University, Brisbane, Australia. He is currently a Ph.D. student at the Department of Computer Science, Dartmouth College, USA. His research interests include data mining, multimedia, database and software engineering.