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 |
|---|---|
| Title of host publication | Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE |
| Pages | 1139-1146 |
| Number of pages | 8 |
| DOIs | |
| 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 |
Other
| Other | 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE |
|---|---|
| Country | United States |
| City | Boston, MA |
| Period | 1/14/07 → 1/17/07 |
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ASJC Scopus subject areas
- Biotechnology
- Genetics
- Bioengineering
Cite this
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.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - MS lesions detection in MRI using grouping artificial immune networks
AU - Younis, Akmal A.
AU - Soliman, Ahmed T.
AU - Kabuka, Mansur R.
AU - John, Nigel M.
PY - 2007/12/1
Y1 - 2007/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=47649092878&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=47649092878&partnerID=8YFLogxK
U2 - 10.1109/BIBE.2007.4375704
DO - 10.1109/BIBE.2007.4375704
M3 - Conference contribution
SN - 1424415098
SN - 9781424415090
SP - 1139
EP - 1146
BT - Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE
ER -