The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Multi-Thresholding of Histopathological Images Using Fuzzy Entropy and Parameterless Cuckoo Search
|
|
Author(s): Krishna Gopal Gopal Dhal (Midnapore College (Autonomous), India), Mandira Sen (Tata Consultancy Services, India)and Sanjoy Das (University of Kalyani, India)
Copyright: 2018
Pages: 18
Source title:
Critical Developments and Applications of Swarm Intelligence
Source Author(s)/Editor(s): Yuhui Shi (Southern University of Science and Technology, China)
DOI: 10.4018/978-1-5225-5134-8.ch013
Purchase
|
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.
Related Content
|
P. Chitra, A. Saleem Raja, V. Sivakumar.
© 2024.
24 pages.
|
|
K. Ezhilarasan, K. Somasundaram, T. Kalaiselvi, Praveenkumar Somasundaram, S. Karthigai Selvi, A. Jeevarekha.
© 2024.
36 pages.
|
|
Kande Archana, V. Kamakshi Prasad, M. Ashok.
© 2024.
17 pages.
|
|
Ritesh Kumar Jain, Kamal Kant Hiran.
© 2024.
23 pages.
|
|
U. Vignesh, R. Elakya.
© 2024.
13 pages.
|
|
S. Karthigai Selvi, R. Siva Shankar, K. Ezhilarasan.
© 2024.
16 pages.
|
|
Vemasani Varshini, Maheswari Raja, Sharath Kumar Jagannathan.
© 2024.
20 pages.
|
|
|