Authors:
Mohamed Mokhtar Bendib
;
Hayet Farida Merouani
and
Fatma Diaba
Affiliation:
Badji-Mokhtar University, Algeria
Keyword(s):
Magnetic Resonance Imaging, Brain MRI Classification, Discrete Wavelet Transform, Undecimated Wavelet Transform, Genetic Programming.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Medical Image Applications
Abstract:
This paper addresses the Brain MRI (Magnetic Resonance Imaging) classification problem from a new
point of view. Indeed, most of the works reported in the literature follow the subsequent methodology: 1)
Discrete Wavelet Transform (DWT) application, 2) sub-band selection, 3) feature extraction, and 4)
learning. Consequently, those methods are limited by the information contained on the selected DWT
outputs (sub-bands). This paper addresses the possibility of creating new suitable DWT sub-bands (by
combining the classical DWT sub-bands) using Genetic Programming (GP) and a Random Forest (RF)
classifier. These could be employed to efficiently address different classification scenarios (normal versus
pathological, one versus all, and even multiclassification) as well as other automatic tasks.