Bidimensional ensemble empirical 6 mode decomposition of functional biomedical images

Positron emission tomography (PET) provides a functional imaging modality to detect signs of dementias in human brains. Two-dimensional empirical mode decomposition (2D EMD) provides means to analyze such images. It extracts characteristic textures from these images which may be fed into powerful cl...

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Bibliographic Details
Main Author: Neubauer, A. (author)
Other Authors: Tomé, A. M. (author), Kodewitz, A. (author), Górriz, J. M. (author), Puntonet, C. G. (author), Lang, E. W. (author)
Format: article
Language:eng
Published: 2014
Subjects:
Multi-dimensional empirical mode decomposition
Positron emission tomography
Support vector machine
Random forest
Dementias
Online Access:http://hdl.handle.net/10773/12069
Country:Portugal
Oai:oai:ria.ua.pt:10773/12069
Description
Summary:Positron emission tomography (PET) provides a functional imaging modality to detect signs of dementias in human brains. Two-dimensional empirical mode decomposition (2D EMD) provides means to analyze such images. It extracts characteristic textures from these images which may be fed into powerful classifiers trained to group these textures into several classes depending on the problem at hand. The study investigates the potential use of 2D EEMD in combination with proper classifiers to form a computer aided diagnosis (CAD) system to assist clinicians in identifying various diseases from functional images alone. PET images of subjects suffering from a dementia are taken to illustrate this ability.

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