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An Improved Firefly Algorithm-Based 2-D Image Thresholding for Brain Image Fusion

An Improved Firefly Algorithm-Based 2-D Image Thresholding for Brain Image Fusion

Srikanth M. V., V. V. K. D. V. Prasad, K. Satya Prasad
Copyright: © 2020 |Volume: 14 |Issue: 3 |Pages: 37
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781799805335|DOI: 10.4018/IJCINI.2020070104
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MLA

Srikanth M. V., et al. "An Improved Firefly Algorithm-Based 2-D Image Thresholding for Brain Image Fusion." IJCINI vol.14, no.3 2020: pp.60-96. http://doi.org/10.4018/IJCINI.2020070104

APA

Srikanth M. V., Prasad, V. V., & Prasad, K. S. (2020). An Improved Firefly Algorithm-Based 2-D Image Thresholding for Brain Image Fusion. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 14(3), 60-96. http://doi.org/10.4018/IJCINI.2020070104

Chicago

Srikanth M. V., V. V. K. D. V. Prasad, and K. Satya Prasad. "An Improved Firefly Algorithm-Based 2-D Image Thresholding for Brain Image Fusion," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 14, no.3: 60-96. http://doi.org/10.4018/IJCINI.2020070104

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Abstract

In this article, an attempt is made to diagnose brain diseases like neoplastic, cerebrovascular, Alzheimer's, and sarcomas by the effective fusion of two images. The two images are fused in three steps. Step 1. Segmentation: The images are segmented on the basis of optimal thresholding, the thresholds are optimized with an improved firefly algorithm (pFA) by assuming Renyi entropy as an objective function. Earlier, image thresholding was performed with a 1-D histogram, but it has been recently observed that a 2-D histogram-based thresholding is better. Step 2: the segmented features are extracted with the scale invariant feature transform (SIFT) algorithm. The SIFT algorithm is good in extracting the features even after image rotation and scaling. Step 3: The fusion rules are made on the basis of an interval type-2 fuzzy set (IT2FL), where uncertainty effects are minimized unlike type-1. The novelty of the proposed work is tested on different benchmark image fusion data sets and has proven better in all measuring parameters.