A Genetic Algorithm Optimized Artificial Neural Network for the Segmentation of MR Images in Frontotemporal Dementia
Frontotemporal Dementia (FTD) is an early onset dementia with atrophy in frontal and temporal regions. The differential diagnosis of FTD remains challenging because of the overlapping behavioral symptoms in patients, which have considerable overlap with Alzheimer’s disease (AD). Neuroimaging analysis especially Magnetic Resonance Image Imaging (MRI) has opened up a new window to identify, and track disease process and progression. In this paper, we introduce a genetic algorithm (GA) tuned Artificial Neural Network (ANN) to measure the structural changes over a period of 1year. GA is a heuristic optimization method based on the Darwin’s principle of natural evolution. The longitudinal atrophy patterns obtained from the proposed approach could serve as a predictor of impending behavioral changes in FTD subjects. The performance of our computerized scheme is evaluated and compared with the ground truth information. Using the proposed approach, we have achieved an average classification accuracy of 95.5 %, 96.5% and 98% for GM, WM and CSF respectively.
KeywordsFrontotemporal Dementia (FTD) Alzheimer’s disease (AD) Magnetic Resonance Imaging (MRI) Genetic algorithm (GA) Artificial Neural Network (ANN)
Unable to display preview. Download preview PDF.
- 2.Go, C., Mioshi, E., Yew, B., Hodges, J.R., Hornberger, M.: Neural correlates of behavioural symptoms in behavioural variant Frontotemporal dementia and Alzheimer’s disease: Employment of a visual MRI rating scale, Dement Neuropsychol. Dement Neuropsychol. 6(1), 12–17 (2012)Google Scholar
- 4.Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley, Massachusetts (1992)Google Scholar
- 11.Ma, H., Zhang, Y., Jia, G.: Medical images segmentation using modified genetic fuzzy clustering algorithm. Computer Engineering and Design 23(13), 2357–2359 (2006)Google Scholar
- 12.Jamshidi, O., Pilevar, A.H.: Automatic Segmentation of Medical Images Using Fuzzy c-Means and the Genetic Algorithm. Journal of Computational Medicine, 1–7 (2013)Google Scholar
- 13.Adams, R., Bischof, L.: Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(6), 641–646 (1994)Google Scholar
- 14.Roslan, R., Jamil, N., Mahmud, R.: Skull Stripping Magnetic Resonance Images Brain Images: Region Growing versus Mathematical Morphology. International Journal of Computer Information Systems and Industrial Management Applications (3), 150–158 (2011) ISSN 2150-7988Google Scholar
- 15.Jafari-Khouzani, K., Siadat, M.R., Soltanian-Zadeh, H., Elisevich, K.: Texture Analysis of Hippocampus for Epilepsy. Proceedings of SPIE (5) (2003)Google Scholar
- 17.Dheeba, J., Tamil Selvi, S.: A CAD System for Breast Cancer Diagnosis Using Modified Genetic Algorithm Optimized Artificial Neural Network. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds.) SEMCCO 2011, Part I. LNCS, vol. 7076, pp. 349–357. Springer, Heidelberg (2011)CrossRefGoogle Scholar
- 18.Goldberg, D.E.: Genetic Algorithm in Search, Optimization and Machine Learning. Addison-Wesley Publishing Company (1989)Google Scholar