An enhancement method for color retinal images based on image formation model. (May 2017)

• Record Type: Journal Article
• Authors: Xiong, Li; Li, Huiqi; Xu, Liang
• Is Part Of: Computer methods and programs in biomedicine. Volume 143(2017)
• Page Start: 137
• Page End: 150
• Publication Date: 2017-05
• Subjects: Contrast enhancement -- Color retinal image -- Image formation model -- Medical image processing; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.02.026
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A new enhancement method based on image formation model is proposed to enhance the color retinal images. This enhancement method can deal with illumination problems, contrast enhancement, and color preservation in color retinal image simultaneously. This enhancement method can improve the poor quality of retinal image caused by different factors, which facilitates the reliable clinic diagnosis. Experimental results indicated that better enhancement image can be produced compared with state-of-art algorithms, especially for blurry retinal images. Abstract: Background and objective: The good quality of color retinal image is essential for doctors to make a reliable diagnose in clinics. Due to major reasons like acquisition process and retinal diseases, most retinal images can show poor illuminance, blur and low contrast, further impeding the process of identifying the underlying retinal condition. Methods: Image formation model of scattering is proposed to enhance color retinal images in this paper. Two parameters of this model, background illuminance and transmission map, are estimated based on extracted background and foreground. The complex nature of the foreground of a retinal image, involving pixels with both low and high intensity, posed a challenge to the proper extraction of these pixels. Therefore, a new method combining Mahalanobis distance discrimination and global spatial entropy-based contrast enhancement is proposed to extract foreground pixels. It extracts background and foreground in high intensity region and low intensity region respectively and it can perform well in blurry image with tiny intensity range. Results: The proposed method is evaluated using 319 color retinal images from three different databases. Experimental results indicated that the proposed method can perform well on illumination problems, contrast enhancement and color preservation. Conclusion: This study proposes a new method of enhancing overall retinal image and produces better enhancement images than several state-of-the-art algorithms, especially for blurry retinal images. This method can facilitate analysis and reliable diagnosis for both ophthalmologists and computer-aided analysis.