Paper Info
Title
PET Image Classification Using HHT-Based Features Through Fractal Sampling.
Abstract
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 representative features from the images, play a decisive role in the design of effective Computer Aided Diagnosis (CAD) systems, which is specially 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 (EMD) 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 spatial relationship among voxels. The devised technique has been used to classify images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) achieving up to a 92% accuracy in a differential diagnosis task (AD vs. controls), which proves that the information retrieved by our methodology is significantly related to the disease.
Year
DOI
Venue
2017
10.1007/978-3-319-59740-9_31
Lecture Notes in Computer Science
Keywords
Field
DocType
Hilbert curve,EEMD,SVM,PET,Alzheimer's Disease
CAD,Voxel,Computer vision,Computer science,Computer-aided diagnosis,Support vector machine,Artificial intelligence,Neuroimaging,Contextual image classification,Discriminative model,Hilbert–Huang transform
Conference
Volume
ISSN
Citations 
10337
0302-9743
1
PageRank 
References 
Authors
0.35
8
6
Name
Order
Citations
PageRank
Andrés Ortiz119525.64
Francisco Lozano261.54
Alberto Peinado323623.65
María J. García-Tarifa410.35
J. M. Górriz557054.40
Javier Ramírez665668.23