# A Hybrid Proposed Fundus Image Enhancement Framework for Diabetic Retinopathy
## 1 Introduction  
Diabetes mellitus (DM) has become a serious healthcare problem and is observed to be the fourth leading factor of death. Recently, the number of diabetic patients was 336 million around the world, and this number is auspicated to reach 592 million by the year 2035 [1]. Diabetic retinopathy (DR) is a microvascular complication of diabetes that causes eyesight loss in various ethnicities of the working-age population. To date, 80% of DR cases have been recorded in patients who have suffered from diabetes for more than 10 years. If the DR patient undergoes early screening and consistent treatment, then the progression of DR can be reduced from severe to the mild stage. The categorizationof DR by clinicians is manifested into two classes, i.e., nonproliferative (NPDR) and proliferative (PDR). According to the clinical estimations in Reference [2] about retina impairment, NPDR is further divided into mild, moderate, and severe stages, and therefore, an early screening of DR severity levels is usually performed using the NPDR subclasses. PDR is an advanced stage where retinal anatomic features such as optic disc, cup, and retinal vascular structure have a large and distorted size [3; 4]. In modern ophthalmology, digital fundus photography is conceived as a powerful medical imaging channel used by ophthalmologists and computer-aided diagnosis (CAD) systems to detect various retinal disorders such as glaucoma, diabetic retinopathy, macular degeneration, and retinal neoplasms [5; 6]. Fundus images normally contain the central and posterior retinal surface, i.e., bright (optic disc and cup) and dark (macula and fovea) regions. Moreover, DR clinical features such as microaneurysms (MAs), hemorrhages (HEMs), exudates (EXs) and cotton wool spots (CWSes) are also available on the fundus images. These images are often processed by clinical experts and screening algorithms to grade and interpret severity levels of DR in patients [7]. Figure 1 presents a visual example of a fundus image with DR-based indicators. The acquisition of fundus images is accomplished using a fundus camera, where fundus images are captured using different light conditions and angles. Therefore, these images suffer from inadequate brightness and contrast, leading to low segmentation and classification results in DR assessment. Figure 2a illustrates the uneven illumination conditions of fundus images, whereas Figure 2b shows a noisy and low contrast fundus image [8]. The automated diagnosis of DR is only possible by the identification of DR-related features on the fundus images. This could be a challenging task because of varied contrast, appearance, and structure of these DR-based lesions. Since MA is defined as an initial clinical indicator of the presence of DR in fundus images, the contrast between the yellowish part (MAs), white region (exudates), and vasculature system is observed to be very low, which makes the segmentation of tiny MAs in the respective fundus part crucial, picmic, and exhaustive for both clinicians and CAD systems. To obtain better real-time settings of DR or to present the fine visual perception of DR-related features, the quality of fundus images needs to be optimized to an acceptable level. Among the parameters of good image quality, contrast is a more significant attribute than dynamic level of brightness and resolution. Contrast enhancement of fundus images has been observed to be an important step by computational algorithms in obtaining better quantitative measurements from the images. Most of the contrast enhancement methods available in the literature are based on spatial (histogram) domains [9; 10]. Figure 1: An example of fundus images where (**a**) represents a normal image; (**b**) microaneurysms (MAs); (**c**) hemorrhages (HEMs) and vessel abnormality; (**d**) exudates (EXs); and (**e**) cotton wool spots (CWSes) [7]. Histogram equalization (HE) is a significant operated image enhancement art due to its easy implementation.