Open AccessFeed‐forward hierarchical model of the ventral visual stream applied to functional brain image classification
Author(s) -
Keator David B.,
Fallon James H.,
Lakatos Anita,
Fowlkes Charless C.,
Potkin Steven G.,
Ihler Alexander
Publication year - 2014
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.22149
Subject(s) - artificial intelligence , computer science , pattern recognition (psychology) , functional imaging , neuroimaging , pipeline (software) , computer vision , machine learning , neuroscience , psychology , programming language
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. ([Serre T, 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. [2005] 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.