Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (available at: adni.loni.ucla.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.ucla.edu/wp-content/uploads/how_to_apply/ADNI_Authorship_List.pdf.
Feed-forward hierarchical model of the ventral visual stream applied to functional brain image classification
Article first published online: 30 JUL 2012
Copyright © 2012 Wiley Periodicals, Inc.
Human Brain Mapping
Volume 35, Issue 1, pages 38–52, January 2014
How to Cite
Keator, D. B., Fallon, J. H., Lakatos, A., Fowlkes, C. C., Potkin, S. G., Ihler, A. and for the Alzheimer's Disease Neuroimaging Initiative (2014), Feed-forward hierarchical model of the ventral visual stream applied to functional brain image classification. Hum. Brain Mapp., 35: 38–52. doi: 10.1002/hbm.22149
- Issue published online: 9 DEC 2013
- Article first published online: 30 JUL 2012
- Manuscript Accepted: 5 JUN 2012
- Manuscript Revised: 1 JUN 2012
- Manuscript Received: 12 MAY 2011
- National Center for Research Resources (NCRR)
- National Institutes of Health (NIH). Grant Number: 1 UL1 RR024150
- NIH Roadmap for Medical Research
- Its contents are solely the responsibility of the authors and do not necessarily represent the official view of NCRR or NIH. Information on NCRR is available at http://www.ncrr.nih.gov/. Information on Reengineering the Clinical Research Enterprise can be obtained from http://nihroadmap. The authors thank the patients and healthy volunteers who participated in the studies supplying the data for this method's evaluation
- Functional Imaging Biomedical Informatics Research Network (National Center for Research Resources). Grant Number: FBIRN U24-RR021992
- Biomedical Informatics Research Network. Grant Number: 1 U24 RR025736-01
- Alzheimer's Disease Neuroimaging Initiative (ADNI)
- National Institute on Aging
- The National Institute of Biomedical Imaging and Bioengineering (NIBIB)
- Pfizer Inc.
- Wyeth Research
- Bristol-Myers Squibb
- Eli Lilly and Company
- Merck & Co. Inc.
- AstraZeneca AB
- Novartis Pharmaceuticals Corporation
- Alzheimer's Association
- Eisai Global Clinical Development
- Elan Corporation plc
- Forest Laboratories
- The Institute for the Study of Aging, with participation from the U.S. Food and Drug Administration. Grant Number: UL1 RR025774
- object recognition;
- Gabor filters;
- template matching;
- brain imaging;
- Alzheimer's disease;
Functional brain imaging is a common tool in monitoring the progression of neurodegenerative and neurological disorders. Identifying functional brain imaging derived features that can accurately detect neurological disease is of primary importance to the medical community. Research in computer vision techniques to identify objects in photographs have reported high accuracies in that domain, but their direct applicability to identifying disease in functional imaging is still under investigation in the medical community. In particular, Serre et al. (2005: In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR-05). pp 994–1000) introduced a biophysically inspired filtering method emulating visual processing in striate cortex which they applied to perform object recognition in photographs. In this work, the model described by Serre et al.  is extended to three-dimensional volumetric images to perform signal detection in functional brain imaging (PET, SPECT). The filter outputs are used to train both neural network and logistic regression classifiers and tested on two distinct datasets: ADNI Alzheimer's disease 2-deoxy-D-glucose (FDG) PET and National Football League players Tc99m HMPAO SPECT. The filtering pipeline is analyzed to identify which steps are most important for classification accuracy. Our results compare favorably with other published classification results and outperform those of a blinded expert human rater, suggesting the utility of this approach. Hum Brain Mapp 35:38–52, 2014. © 2012 Wiley Periodicals, Inc.