Medical image classification is currently a challenging task
that can be used to aid the diagnosis of different brain diseases. Thus,
exploratory and discriminative analysis techniques aiming to obtain rep-
resentative features from the images, play a decisive role in the design
of effective Computer Aided Diagnosis (CAD) systems, which is spe-
cially important in the early diagnosis of dementias. In this work we
present a technique that allows extracting discriminative features from
Positron Emission Tomography (PET) by means of an Empirical Mode
Decomposition-based (EEMD) method. This requires to transform the
3D PET image into a time series which is addressed by sampling the
image using a fractal-based method which allows to preserve the spa-
tial relationship among voxels. The devised technique has been used
to classify images from the Alzheimer's Disease Neuroimaging Initiat-
ive (ADNI) achieving up to a 90.5% accuracy in a differential diagnosis
task (AD vs. controls), which proves that the information retrieved by
our methodology is significantly linked to the disease.
