Please use this identifier to cite or link to this item:
http://hdl.handle.net/10773/12069
Title: | Bidimensional ensemble empirical 6 mode decomposition of functional biomedical images |
Author: | Neubauer, A. Tomé, A. M. Kodewitz, A. Górriz, J. M. Puntonet, C. G. Lang, E. W. |
Keywords: | Multi-dimensional empirical mode decomposition Positron emission tomography Support vector machine Random forest Dementias |
Issue Date: | Jan-2014 |
Publisher: | World Scientific |
Abstract: | 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. |
Peer review: | yes |
URI: | http://hdl.handle.net/10773/12069 |
DOI: | 10.1142/S1793536914500046 |
ISSN: | 1793-5369 |
Appears in Collections: | IEETA - Artigos |
Files in This Item:
File | Description | Size | Format | |
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Proofs_2DEEMD.pdf | 6.52 MB | Adobe PDF |
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