Texton Based Diagnosis Of Alzheimer's Disease
Pedro Morgado, Margarida Silveira, Durval Campos Costa, Alzheimer's Disease Neuroimaging Initiative

The textural content of FDG-PET brain images has been shown to
be useful for the diagnosis of Alzheimer"s disease (AD) and Mild
Cognitive Impairment (MCI). In this paper, we investigate the use of
the textons method [1], a powerful texture extraction procedure that
uses a full statistical representation of the response of the image to
a set of filters. We also extend the MR8 filter bank used in [1] to
3D in order to match the dimensionality of FDG-PET images, while
maintaining important properties such as invariance to rotation and
a low dimensionality of the filter response space. We propose two
methods to tackle difficulties inherent to the extraction and classification
of texture from images whose appearance varies over space
and to the fact that most regions of the image are not affected by
AD or MCI. The first method selects only the voxels with the most
discriminative filter responses, while the second method focuses on
brain regions manually labeled by an expert physician. Experiments
showed that the proposed approaches outperformed the more common
one that uses voxel intensities directly as features both in the
diagnosis of AD and MCI. It was also observed that the discriminative
power of certain brain regions increased significantly when the
texton based analysis was performed.