A Hybrid Proposed Fundus Image Enhancement Framework for Diabetic Retinopathy

Abstract

Diabetic retinopathy (DR) is a complication of diabetes and is known as visual impairment, and is diagnosed in various ethnicities of the working-age population worldwide. Fundus angiography is a widely applicable modality used by ophthalmologists and computerized applications to detect DR-based clinical features such as microaneurysms (MAs), hemorrhages (HEMs), and exudates (EXs) for early screening of DR. Fundus images are usually acquired using funduscopic cameras in varied light conditions and angles. Therefore, these images are prone to non-uniform illumination, poor contrast, transmission error, low brightness, and noise problems. This paper presents a novel and real-time mechanism of fundus image enhancement used for early grading of diabetic retinopathy, macular degeneration, retinal neoplasms, and choroid disruptions. The proposed system is based on two folds: (i) An RGB fundus image is initially taken and converted into a color appearance module (called lightness and denoted as J) of the CIECAM02 color space model to obtain image information in grayscale with bright light. Afterwards, in step (ii), the achieved J component is processed using a nonlinear contrast enhancement approach to improve the textural and color features of the fundus image without any further extraction steps. To test and evaluate the strength of the proposed technique, several performance and quality parameters—namely peak signal-to-noise ratio (PSNR), contrast-to-noise ratio (CNR), entropy (content information), histograms (intensity variation), and a structure similarity index measure (SSIM)—were applied to 1240 fundus images comprised of two publicly available datasets, DRIVE and MESSIDOR. It was determined from the experiments that the proposed enhancement procedure outperformed histogram-based approaches in terms of contrast, sharpness of fundus features, and brightness. This further revealed that it can be a suitable preprocessing tool for segmentation and classification of DR-related features algorithms.

Automatic Detection of Diabetic Retinopathy: A Review on Datasets, Methods and Evaluation Metrics

“…It is useful to identify disease levels based on numerical value with various thresholds. Furthermore, it can also be applied to classify the results of DR in terms of quantitative measurements [153].…”

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

“…It is a numeric value consisting of different thresholds used to detect disease levels. Therefore, it can be a useful option for clinicians to employ them in the measurement of DR classification results [153][154][155][156].…”

Detection of Early Signs of Diabetic Retinopathy Based on Textural and Morphological Information in Fundus Images

“…There are two machine learning paradigms to try to solve the problem: the traditional classification approach where the input is a feature vector obtained from the fundus images [7][8][9], and the deep learning approach (in particular, using convolutional neural networks) [10][11][12]. While the second approach usually gets better classification results, most of these methods do not provide understandable interpretations about the relevant features of the different pathological signs in the retina, so its clinical usefulness is questionable until more research efforts in the interpretation of the high-level features extracted by the convolutional blocks of a CNN will be done.…”