An adaptive framework of De-noising and enhancement for fundus imaging using enhanced guided filter and non-illumination correction method for diabetic retinopathy
Affiliations: [a]
Department of CS&SE, Andhra University-India
| [b]
Department of CSE, GITAM University (deemed to be)-India
Correspondence:
[*]
Corresponding author. N.V. Naik, Research Scholar, Department of CS&SE, Andhra University-India. Email: [email protected].
Abstract: Retinal fundus imaging has been used in the diagnosis of many cardiovascular and retinal-related diseases like age-related macular degeneration, pathological myopia, diabetic retinopathy, and glaucoma. In recent years, the computerized technique is adapted to fundus image analysis and processing for quick diagnosis. However, low-quality retinal pictures are produced when certain eye disorders and photographic circumstances exist. Poor-quality retinal pictures of this kind are not helpful for diagnosis, particularly when using automated image analysis software. The causes for degradation include low illumination, high illumination, image blurring, uneven illumination, low contrast, and color distortion. Several image-enhancing techniques have been developed to address this; however, they may result in sudden changes in color levels, false borders, and the loss of picture detail, particularly in retinal images. To prevent these negative consequences, this paper proposed a novel enhancement algorithm EGF_AGC (Enhanced Guided Filter+Adaptive Gamma Correction) for color fundus retinal images. The proposed method is worked is described in four steps. The first stage of the recommended procedure is to utilize a hybrid filter to enhance the retinal pictures’ appearance details. Next, applied enhanced guided filter method to the detailed retina images to enhance the image quality. Moreover, the suggested technique also worked to retain uneven illumination issues raised in retina images using the Adaptive Gamma Correction method. The final improved retinal pictures are created by combining the phases before utilizing the HSV color approach. The databases of retinal images, which include the STARE, the DRIVE, and the CHBASE_DB1 database, were utilized to determine image enhancement effects. The findings were compared with CLAHE, Bilateral Filter, Guided Filter, and Gamma Correction retinal enhancement methods. Compared to similar enhancement approaches, experiments showed that our method could give a competitive outcome and eliminate degradation caused by low-quality fundus pictures.
