Application of soft computing techniques in dental Image Segmentation
Journal Title: International Journal of engineering Research and Applications - Year 2018, Vol 8, Issue 9
Abstract
Image segmentation is a very important and pre-processing step in image analysis. The conventional multilevel thresholding methods are efficient for bi-level thresholding because of its simplicity, robustness, less convergence time and accuracy. However, a mass of computational cost is needed and efficiency is broken down as an exhaustive search is utilized for finding the optimal thresholds, which results in application of evolutionary algorithm and swarm intelligence to obtain the optimal thresholds. The main aim of image segmentation is to segregate the foreground from background. For the first time this paper established a naturally inspired hybrid optimization based multilevel image thresholding for image segmentation by maximizing Shannon entropy or Fuzzy entropy. The proposed algorithm is tested on standard set of medical dental images andit is demonstrated that the proposed method shows better performance in objective function, structural similarity index, and misclassification error than state of art methods.
Authors and Affiliations
M. S. R. Naidu, P. Rajesh Kumar
Keywords
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- EP ID EP395131
- DOI 10.9790/9622-0809014550.
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