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Multi-Thresholding of Histopathological Images Using Fuzzy Entropy and Parameterless Cuckoo Search

Multi-Thresholding of Histopathological Images Using Fuzzy Entropy and Parameterless Cuckoo Search

Krishna Gopal Gopal Dhal, Mandira Sen, Sanjoy Das
Copyright: © 2018 |Pages: 18
ISBN13: 9781522551348|ISBN10: 1522551344|EISBN13: 9781522551355
DOI: 10.4018/978-1-5225-5134-8.ch013
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MLA

Dhal, Krishna Gopal Gopal, et al. "Multi-Thresholding of Histopathological Images Using Fuzzy Entropy and Parameterless Cuckoo Search." Critical Developments and Applications of Swarm Intelligence, edited by Yuhui Shi, IGI Global, 2018, pp. 339-356. https://doi.org/10.4018/978-1-5225-5134-8.ch013

APA

Dhal, K. G., Sen, M., & Das, S. (2018). Multi-Thresholding of Histopathological Images Using Fuzzy Entropy and Parameterless Cuckoo Search. In Y. Shi (Ed.), Critical Developments and Applications of Swarm Intelligence (pp. 339-356). IGI Global. https://doi.org/10.4018/978-1-5225-5134-8.ch013

Chicago

Dhal, Krishna Gopal Gopal, Mandira Sen, and Sanjoy Das. "Multi-Thresholding of Histopathological Images Using Fuzzy Entropy and Parameterless Cuckoo Search." In Critical Developments and Applications of Swarm Intelligence, edited by Yuhui Shi, 339-356. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-5134-8.ch013

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Abstract

This chapter presents a multi-level histopathological image thresholding approach based on fuzzy entropy theory. This entropy measure is maximized to obtain the optimal thresholds of the image. In order to solve this problem, one self-adaptive and parameter-less cuckoo search (CS) algorithm has been employed, which leads to an accurate convergence towards the optima within less computational time. The performance of the proposed CS is also compared with traditional CS (TCS) algorithm and particle swarm optimization (PSO). The outcomes of the proposed fuzzy entropy-based model are compared with Shannon entropy-based model both visually and statistically in order to establish the perceptible difference in image.

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