Brain Tumor Segmentation in Magnetic Resonance Images using Genetic Algorithm Clustering and AdaBoost Classifier

Gustavo C. Oliveira, Renato Varoto, Alberto Cliquet Jr.

Abstract

We present a technique for automatic brain tumor segmentation in magnetic resonance images, combining a modified version of a Genetic Algorithm Clustering method with an AdaBoost Classifier. In a group of 42 FLAIR images, segmentations produced by the algorithm were compared to the ground truth information produced by radiologists. The mean Dice similarity coefficient reached by the algorithm was 70.3%. In most cases, the AdaBoost classifier increased the quality of the segmentation, improving, on average, the DSC in about 10%. Our implementation of the Genetic Algorithm Clustering method presents improvements compared to the original method. The use of a fixed, small number of groups and smaller population allowed for less computational effort. In addition, adaptive restriction in the initial segmentation was achieved by using the information of the groups with highest and 2nd-highest mean intensities. By exploring intensity and spatial information of the pixels, the AdaBoost classifier improved segmentation results.

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Paper Citation


in Harvard Style

C. Oliveira G., Varoto R. and Cliquet Jr. A. (2018). Brain Tumor Segmentation in Magnetic Resonance Images using Genetic Algorithm Clustering and AdaBoost Classifier.In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, ISBN 978-989-758-278-3, pages 77-82. DOI: 10.5220/0006534900770082


in Bibtex Style

@conference{bioimaging18,
author={Gustavo C. Oliveira and Renato Varoto and Alberto Cliquet Jr.},
title={Brain Tumor Segmentation in Magnetic Resonance Images using Genetic Algorithm Clustering and AdaBoost Classifier},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING,},
year={2018},
pages={77-82},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006534900770082},
isbn={978-989-758-278-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING,
TI - Brain Tumor Segmentation in Magnetic Resonance Images using Genetic Algorithm Clustering and AdaBoost Classifier
SN - 978-989-758-278-3
AU - C. Oliveira G.
AU - Varoto R.
AU - Cliquet Jr. A.
PY - 2018
SP - 77
EP - 82
DO - 10.5220/0006534900770082