Performance of grey level statistic features versus Gabor wavelet for screening MRI brain tumors : a comparative study

Conference paper

Hasan, A, Meziane, F. and Jalab, HA 2016. Performance of grey level statistic features versus Gabor wavelet for screening MRI brain tumors : a comparative study. 2016 6th International Conference on Information Communication and Management (ICICM). Hatfield, UK 29 - 31 Oct 2016 IEEE Xplore. https://doi.org/10.1109/infocoman.2016.7784230
AuthorsHasan, A, Meziane, F. and Jalab, HA
TypeConference paper
Abstract

Medical imaging technologies have an important role in the care of all human’s organs and disease entities, where they are used widely for the effective diagnosis, treatment and monitoring of the disease. The MRI has been among the most important of all these technologies in the care of patients with brain tumors, where the brain tumor is the one of the most common diseases that cause the death. Screening of brain tumors is an essential to significant improvements in the diagnose and reduce the incidence of death, it can only be as successful as the feature extraction techniques it relies on. Many of these techniques have been used, but it is still not exactly clear which of feature extraction techniques ought to be favored. In this paper, we present here the results of a study in which we compare the proficiency of utilizing grey level statistic method and Gabor wavelet method in detecting and recognizing MRI brain abnormality. The framework that serves as our testbed includes med-sagittal plane detection and correction, feature extraction, feature selection, and lastly classification and comparison.

KeywordsLinear Discriminant Analysis; ; magnetic resonance imaging; ; modified grey level Co-occurrence Matrix; ; Multi-layer perceptron Neural Network; ; Support Vector Machine
Year2016
Conference2016 6th International Conference on Information Communication and Management (ICICM)
JournalInternational Conference on Information Communication and Management
PublisherIEEE Xplore
Digital Object Identifier (DOI)https://doi.org/10.1109/infocoman.2016.7784230
Web address (URL)http://dx.doi.org/10.1109/INFOCOMAN.2016.7784230
Journal citation(Article: 7784230), pp. 136 - 140
ISBN978-1-5090-3495-6
Web address (URL) of conference proceedingshttps://ieeexplore.ieee.org/xpl/conhome/7764616/proceeding
Publication dates15 Dec 2016
Publication process dates
Deposited05 Jun 2023
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