Multilevel Image Thresholding for Image Segmentation using Hybrid Algorithm
M.S.R. Naidu1, P. Rajesh Kumar2
1M.S.R.Naidu, Department of Electronics & Communication Engineering, Aditya Institute Of Technology And Management, Tekkali, Srikakaulam, India.
2Dr. P. Rajesh Kumar, Department of Electronics & Communication Engineering, A.U.College of Engineering, Andhra university, Visakhaptnam.
Manuscript received on October 13, 2019. | Revised Manuscript received on 24 October, 2019. | Manuscript published on November 10, 2019. | PP: 4272-4279 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4847119119/2019©BEIESP | DOI: 10.35940/ijitee.A4847.119119
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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: Image thresholding is an extraction method of objects from a background scene, which is used most of the time to evaluate and interpret images because of their advanced simplicity, robustness, time reduced, and precision. The main objective is to distinguish the subject from the background of the image segmentation. As the ordinary image segmentation threshold approach is computerized costly while the necessity for optimization techniques are highly recommended for multi-tier image thresholds. Level object segmentation threshold by using Shannon entropy and Fuzzy entropy maximized with hGSA-PS. An entropy maximization of hGSA-PS dependent multilevel image thresholds is developed, where the results are best demonstrated in PSNR, misclassification, structural similarity index and segmented image quality compared to the Firefly algorithm, adaptive cuckoo search algorithm and the search algorithm gravitational.
Keywords: Entropy, Image thresholding, PNR, SSIM.
Scope of the Article: Algorithm Engineering