MS lesions detection in MRI using grouping artificial immune networks

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

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish (US)
Title of host publicationProceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE
Pages1139-1146
Number of pages8
DOIs
StatePublished - Dec 1 2007
Event7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE - Boston, MA, United States
Duration: Jan 14 2007Jan 17 2007

Publication series

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

Other

Other7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE
CountryUnited States
CityBoston, MA
Period1/14/071/17/07

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ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Bioengineering

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