Optimal Threshold Based Brain Image Fusion for Brain Cancer Detection u sing Firefly algorithm
M. V. Srikanth1, V. V. K. D. V. Prasad2, K. Satya Prasad3 

1M.V. Srikanth, ECE, Gudlavalleru Engineering College, Gudlavalleru, A.P, India.
2V.V.K.D.V. Prasad, ECE, Gudlavalleru Engineering College, Gudlavalleru , A.P, India.
3K. Satya Prasad, RECTOR, Vignan University, Guntur, A.P, India.

Manuscript received on 12 March 2019 | Revised Manuscript received on 17 March 2019 | Manuscript published on 30 July 2019 | PP: 2750-2759 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2143078219/19©BEIESP | DOI: 10.35940/ijrte.B2143.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In this paper an attempt is made to diagnose brain disease like neoplastic disease, cerebrovascular disease, Alzheimer disease, fatal disease, Sarcoma disease by effective fusion of two images. Two images are fused in three steps: Step 1. Segmentation The images are segmented on the basis of optimal thresholding; thresholds are optimized with natural inspired firefly algorithm by assuming fuzzy entropy as objective function. Image thresholding is one of the segmentation techniques which is flexible, simple and has less convergence time as compared to others. Step 2: the segmented features are extracted with Scale Invariant Feature Transform (SIFT) algorithm. The SIFT algorithm is good in extracting the features even after image rotation and scaling. Step 3: Finally fusion rules are made on the basis of interval type-2 fuzzy (IT2FL), where uncertainty effects are minimized unlike type-1. The novelty of the proposed work is tested on different benchmark Image fusion data set and proved better in all measuring parameters.
Index Terms: Fuzzy Entropy; Image Fusion; Firefly Algorithm; Scale Invariant Feature Transform; Interval Type-2 Fuzzy.

Scope of the Article: Fuzzy Logics