MS lesions detection in MRI using grouping artificial immune networks

Akmal A. Younis; Ahmed T. Soliman; Mansur R. Kabuka; Nigel M. John

doi:10.1109/BIBE.2007.4375704

MS lesions detection in MRI using grouping artificial immune networks

Akmal A. Younis, Ahmed T. Soliman, Mansur R. Kabuka, Nigel M. John

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 Scopus citations

Abstract

A dual-channel 3D MRI segmentation technique based on Grouping Artificial Immune Networks (GAIN) is introduced to detect MS lesion in MR images. The technique demonstrates the ability of artificial immune networks to handle MS lesions detection in T1- and T2-weighted brain MRI. The GAIN-based MRI segmentation technique was evaluated using simulated MS brain images from the McConnell Brain Imaging Centre, Montreal Neurological Institute of McGill University. 3D anisotropic filtering is used to handle noise artifacts in the simulated 3D MRI data sets. Experimental results demonstrated that dual channel MS segmentation approach exhibited high accuracy in segmenting the simulated MS brain data and an even higher accuracy when compared to techniques based on single channel 3D MRI data sets in terms of the Dice coefficient, an objective measure of overlap.

Original language	English (US)
Title of host publication	Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE
Pages	1139-1146
Number of pages	8
DOIs	https://doi.org/10.1109/BIBE.2007.4375704
State	Published - Dec 1 2007
Event	7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE - Boston, MA, United States Duration: Jan 14 2007 → Jan 17 2007

Publication series

Name	Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE

Other

Other	7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE
Country/Territory	United States
City	Boston, MA
Period	1/14/07 → 1/17/07

ASJC Scopus subject areas

Biotechnology
Genetics
Bioengineering

Access to Document

10.1109/BIBE.2007.4375704

Cite this

Younis, A. A., Soliman, A. T., Kabuka, M. R., & John, N. M. (2007). MS lesions detection in MRI using grouping artificial immune networks. In Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE (pp. 1139-1146). [4375704] (Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE). https://doi.org/10.1109/BIBE.2007.4375704

MS lesions detection in MRI using grouping artificial immune networks. / Younis, Akmal A.; Soliman, Ahmed T.; Kabuka, Mansur R.; John, Nigel M.

Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE. 2007. p. 1139-1146 4375704 (Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Younis, AA, Soliman, AT, Kabuka, MR & John, NM 2007, MS lesions detection in MRI using grouping artificial immune networks. in Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE., 4375704, Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE, pp. 1139-1146, 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE, Boston, MA, United States, 1/14/07. https://doi.org/10.1109/BIBE.2007.4375704

Younis AA, Soliman AT, Kabuka MR, John NM. MS lesions detection in MRI using grouping artificial immune networks. In Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE. 2007. p. 1139-1146. 4375704. (Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE). https://doi.org/10.1109/BIBE.2007.4375704

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abstract = "A dual-channel 3D MRI segmentation technique based on Grouping Artificial Immune Networks (GAIN) is introduced to detect MS lesion in MR images. The technique demonstrates the ability of artificial immune networks to handle MS lesions detection in T1- and T2-weighted brain MRI. The GAIN-based MRI segmentation technique was evaluated using simulated MS brain images from the McConnell Brain Imaging Centre, Montreal Neurological Institute of McGill University. 3D anisotropic filtering is used to handle noise artifacts in the simulated 3D MRI data sets. Experimental results demonstrated that dual channel MS segmentation approach exhibited high accuracy in segmenting the simulated MS brain data and an even higher accuracy when compared to techniques based on single channel 3D MRI data sets in terms of the Dice coefficient, an objective measure of overlap.",

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MS lesions detection in MRI using grouping artificial immune networks

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