A Hybrid Moth-Flame Optimization Technique for Feature Selection in Brain Image Classification and Image Denoising by Improved Log Gabor Filter

P. M. Diaz, M. Julie Emerald Jiju

Source Title: International Journal of Computer Vision and Image Processing (IJCVIP)12(1)

ISSN: 2155-6997|EISSN: 2155-6989|EISBN13: 9781683182122|DOI: 10.4018/IJCVIP.296585

MLA

Diaz, P. M., and M. Julie Emerald Jiju. "A Hybrid Moth-Flame Optimization Technique for Feature Selection in Brain Image Classification and Image Denoising by Improved Log Gabor Filter." IJCVIP vol.12, no.1 2022: pp.1-20. http://doi.org/10.4018/IJCVIP.296585

APA

Diaz, P. M. & Jiju, M. J. (2022). A Hybrid Moth-Flame Optimization Technique for Feature Selection in Brain Image Classification and Image Denoising by Improved Log Gabor Filter. International Journal of Computer Vision and Image Processing (IJCVIP), 12(1), 1-20. http://doi.org/10.4018/IJCVIP.296585

Chicago

Diaz, P. M., and M. Julie Emerald Jiju. "A Hybrid Moth-Flame Optimization Technique for Feature Selection in Brain Image Classification and Image Denoising by Improved Log Gabor Filter," International Journal of Computer Vision and Image Processing (IJCVIP) 12, no.1: 1-20. http://doi.org/10.4018/IJCVIP.296585

Export Reference

Favorite Full-Issue Download

View Full Text HTML

View Full Text PDF

Abstract

In brain image classification, feature set reduction is essential to build an optimised feature subset that will lead to precise measurement. In this paper, an improved technique for feature selection by Moth Flame Optimization with Opposition Based Learning (OBL) and Simulated Annealing (OB-MFOSA) is proposed. The OBL strategy is used to create the optimum initial solution, while Simulated Annealing improves the search space. The proposed OB-MFOSA shows improved performance than other well-known existing algorithms by eliminating getting stuck in the local optima. By using this hybrid moth flame optimization, the feature set is reduced to 40%. Also, image denoising is performed by Dual Tree Complex Wavelet Transform (DTCWT) with an improved Log Gabor filtering technique. The filter bank of Log Gabor filter bank is tuned by Genetic Algorithm. The selected features from hybrid MFO algorithm are classified using SVM classifier. Experiments reveal that this hybrid algorithm shows accurate classification outputs than the previous methods.