Enhancement of Microscopy Images by Using a Hybrid Technique Based on Adaptive Histogram Equalisation and Fuzzy Logic

Abstract

The enhancement of colour medical microscopy images plays an important role in biological fields. Colour medical microscopy images have the lack of contrast and lighting. In this paper, a robust algorithm for enhancing microscopy images was proposed. In the proposed method, the image was first improved by using Contrast-Limited Adaptive Histogram Equalisation (CLAHE). Then, the luminance channel in the colour space was enhanced on the basis of sigmoid transformation and fuzzy logic. Data containing 50 medical microscopy image forms were studied, and the suggested method for enhancement was compared with several other methods, such as principal component analysis using the reflection model, fuzzy logic by stretch membership function, CLAHE, modified colour histogram equalization, contrast-enhancement based on median-mean based sub image-clipped histogram equalization and retinex method by colour restoration. The collected results illustrated that the suggested method gets excellent quality averages in terms of entropy value (7.96), average gradient (14.57) and mean of standard division (62.80).

Optical Microscopy Images Enhancement Using YCbCr Color Space Based on Nonlinear Mapping

“…Although the restored image remained pale, the procedure produced better results. Hind A. Majeed et al [8] suggested a dialogue algorithm to enhance a medical image taken by a light microscope that has poor contrast by enhancing illumination in the color space using a first and second histogram equation based on the sigmoid, and the study then demonstrated transformation and fuzzy logic.…”