A two-stage histogram equalization enhancement scheme for feature preservation in retinal fundus images. (February 2023)

• Record Type: Journal Article
• Authors: Sule, Olubunmi O.; Ezugwu, Absalom E.
• Is Part Of: Biomedical signal processing and control. Volume 80:Part 2(2023)
• Publication Date: 2023-02
• Subjects: Clip-limit -- Tile size -- CLAHE -- Feature Similarity Index Measure (FSIM) -- Retinal fundus images -- Image enhancement; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.104384
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Proposal of enhancement techniques for inconsistent contrast and colour imbalance of Retinal fundus images. A histogram matching technique is employed for intra-luminosity and colour enhancement. Adoption of CLAHE for the local contrast enhancement of the retinal fundus image. The proposed method requires low computational resources, complexity, time, and cost. Abstract: Early detection of ophthalmologic complications in the retinal fundus images is crucial and requires a quality image for accurate analysis and diagnosis. This often proves tricky due to the visual complexities of some retinal fundus images resulting in over-enhancement, non-uniform contrast, artificial boundaries, abrupt colour change, and other undesirable limitations in the performance of the existing image enhancement methods. In this paper, the uneven illumination and colour balance are globally adjusted using a histogram match. At the same time, the luminance component (V) of the HSV colour space is equalized with CLAHE for local contrast enhancement. Eight control parameter values are investigated to choose the most suitable values to restrict excessive enhancement and control the quality of the enhanced image. The performance of the proposed enhancement algorithm on the STARE, DRIVE, HRF, and DIARETDB1 databases shows that the method can prominently enhance colour retinal fundus images. The visual and quantitative comparisons of the proposed method with state-of-the-art techniques are remarkably outstanding. The statistical analysis test achieves a ρ -value far<0.05 justifying the statistical significance of the quantitative metrics and the high-quality performance of our enhancement framework.