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Contrast limited fuzzy adaptive histogram equalization for enhancement of brain images
Author(s) -
Magudeeswaran V.,
Singh J. Fenshia
Publication year - 2017
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.22214
Subject(s) - adaptive histogram equalization , histogram equalization , artificial intelligence , contrast (vision) , histogram , computer science , fuzzy logic , gray level , pattern recognition (psychology) , histogram matching , brightness , computer vision , contrast enhancement , equalization (audio) , entropy (arrow of time) , mathematics , algorithm , image (mathematics) , optics , physics , medicine , decoding methods , quantum mechanics , magnetic resonance imaging , radiology
Contrast limited fuzzy adaptive histogram equalization (CLFAHE) is proposed to improve the contrast of MRI Brain images. The proposed method consists of three stages. First, the gray level intensities are transformed into membership plane and membership plane is modified with Contrast intensification operator. In the second stage, the contrast limited adaptive histogram equalization is applied to the modified membership plane to prevent excessive enhancement in contrast by preserving the original brightness. Finally, membership plane is mapped back to the gray level intensities. The performance of proposed method is evaluated and compared with the existing methods in terms of qualitative measures such as entropy, PSNR, AMBE, and FSIM. The proposed method provides enhanced results by giving better contrast enhancement and preserving the local information of the original image. © 2017 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 27, 98–103, 2017