Natural Image Bases to Represent Neuroimaging Data

Murat Seçkin Ayhan, Anthony S. Maida

Abstract:

Visual inspection of neuroimagery is susceptible to human eye limitations. Computerized methods have been shown to be equally or
more efective than human clinicians in diagnosing dementia from neuroimages. Nevertheless, much of the work involves the use
of domain expertise to extract hand–crafted features. The key technique in this paper is the use of cross–domain features to represent
MRI data. We used a sparse autoencoder to learn a set of bases from natural images and then applied convolution to extract features
from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Using this new representation, we classify MRI instances
into three categories: Alzheimer’s Disease (AD), Mild Cognitive Impairment (MCI) and Healthy Control (HC). Our approach, in spite
of being very simple, achieved high classification performance, which is competitive with or better than other approaches.