research-article

Performance evaluation of feature extraction techniques in MR-Brain image classification system

Authors:

Mohammed Khalil,

Habib Ayad,

Abdellah AdibAuthors Info & Claims

Procedia Computer Science, Volume 127, Issue C

Pages 218 - 225

https://doi.org/10.1016/j.procs.2018.01.117

Published: 01 May 2018 Publication History

Abstract

In this paper, we present a MR-Brain image classification system to classify a given MR-brain image as normal or abnormal. This system first employs three feature extraction techniques namely, Gray-Level Co-Occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Histogram of Oriented Gradient (HOG). The obtained feature vector of each technique is passed through a k-Nearest Neighbor (k-NN) classifier. The resulting dissimilarity measure values of the classifiers are combined then by a fusion operator in order to increase the classification accuracy. Two benchmark MR image datasets, Dataset-66 and Dataset-160, have been used to validate the system performance. A cross-validation scheme is adopted to improve the generalization capability of the system. The obtained simulation results are compared with those ones of the existing methods to evaluate the performance of the presented MR-Brain classification system.

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Cited By

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Ghasemi MKelarestaghi MEshghi FSharifi A(2021)AFDL: a new adaptive fuzzy dictionary learning for medical image classificationPattern Analysis & Applications10.1007/s10044-020-00909-124:1(145-164)Online publication date: 1-Feb-2021
https://dl.acm.org/doi/10.1007/s10044-020-00909-1
Huang ZSong GZhao Y(2019)Brain Tumor Screening using Adaptive Gamma Correction and Deep LearningProceedings of the 2019 8th International Conference on Bioinformatics and Biomedical Science10.1145/3369166.3369184(47-53)Online publication date: 23-Oct-2019
https://dl.acm.org/doi/10.1145/3369166.3369184

Performance evaluation of feature extraction techniques in MR-Brain image classification system
1. Computing methodologies
  1. Machine learning

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Published In

Procedia Computer Science Volume 127, Issue C

May 2018

552 pages

ISSN:1877-0509

EISSN:1877-0509

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Elsevier Science Publishers B. V.

Netherlands

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Published: 01 May 2018

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Ghasemi MKelarestaghi MEshghi FSharifi A(2021)AFDL: a new adaptive fuzzy dictionary learning for medical image classificationPattern Analysis & Applications10.1007/s10044-020-00909-124:1(145-164)Online publication date: 1-Feb-2021
https://dl.acm.org/doi/10.1007/s10044-020-00909-1
Huang ZSong GZhao Y(2019)Brain Tumor Screening using Adaptive Gamma Correction and Deep LearningProceedings of the 2019 8th International Conference on Bioinformatics and Biomedical Science10.1145/3369166.3369184(47-53)Online publication date: 23-Oct-2019
https://dl.acm.org/doi/10.1145/3369166.3369184

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Application of Feature Extraction and Classification Methods for Histopathological Image using GLCM, LBP, LBGLCM, GLRLM and SFTA

Performance comparison of various machine learning classifiers using fusion of LBP, intensity and GLCM feature extraction techniques for thyroid nodules classification

Scene Classification Using Multi-Resolution WAHOLB Features and Neural Network Classifier

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