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Impulse noise reduction using hybrid neuro‐fuzzy filter with improved firefly algorithm from X‐ray bio‐images
Author(s) -
Pugalenthi R.,
Oliver A. Sheryl,
Anuradha M.
Publication year - 2020
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22453
Subject(s) - firefly algorithm , impulse noise , computer science , noise reduction , algorithm , noise (video) , median filter , reduction (mathematics) , particle swarm optimization , control theory (sociology) , artificial intelligence , mathematics , image processing , pixel , geometry , image (mathematics) , control (management)
Noise filtering performance in medical images is improved using a neuro‐fuzy network developed with the combination of a post processor and two neuro‐fuzzy (NF) filters. By the fact, the Sugeno‐type is found to be less accurate during impulse noise reduction process. In this paper, we propose an improved firefly algorithm based hybrid neuro‐fuzzy filter in both the NF filters to improve noise reduction performance. The proposed noise reduction system combines the advantages of the neural, fuzzy and firefly algorithms. In addition, an improved version of firefly algorithm called searching diversity based particle swarm firefly algorithm is used to reduce the local trapping problem as well as to determine the optimal shape of membership function in fuzzy system. Experimental results show that the proposed filter has proved its effectiveness on reducing the impulse noise in medical images against different impulse noise density levels.