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Eye diseases that are common and many diseases that result in visual ailments, such as diabetes and vascular disease, can be diagnosed through retinal imaging. The enhancement of retinal images often helps in diagnosing diseases related to retinal organ failure. However, today's image enhancement methods may lead to artificial boundaries, sudden color gradation, and the loss of image details. Therefore, to prevent these side effects, a new method of retinal image enhancement is proposed. In this work, we propose a new method for enhancing the overall contrast of colored retinal images. That is, we propose low-light image enhancement using a new retinex method based on a powerful semidecoupled retinex method. In particular, illumination layer I gradually approximates the S input image according to the file. This leads to a complete Gaussian transformation model, while the R-layer reflectance is estimated jointly by S and intermediary by I to suppress image noise simultaneously during R estimation on the publicly available Messidor database. From our assessment measurements (PSNR and SSIM), we show that this proposed method is effective in comparison with the relevant and recently proposed retinal imaging methods; moreover, the color, which is determined by the data, does not change the image structure. Finally, a technique is presented to improve the pronounced color of a retinal image, which is useful for ophthalmologists to screen for retinal disease more effectively. Moreover, this technique can be used in the development of robotics for imaging tests to search for clinical markers. [ABSTRACT FROM AUTHOR]

Proper contrast and sufficient illuminance are important in clearly identifying the retinal structures, while the required quality cannot always be guaranteed due to major reasons like acquisition process and diseases. To ensure the effectiveness of enhancement, two solutions are developed for blurry retinal images with sufficient illuminance and insufficient illuminance, respectively. The proposed contrast stretching and intensity transfer are main steps in both of the two solutions. The contrast stretching is based on base-intensity removal and non-uniform addition. We assume that a base-intensity exists in an image, which mainly supports the basic illuminance but has less contribution to texture information. The base-intensity is estimated by the constrained Gaussian function and then removed. The non-uniform addition using compressed Gamma map is further developed to improve the contrast. Additionally, an effective intensity transfer strategy is introduced, which can provide required illuminance for a single channel after contrast stretching. The color correction can be achieved if the intensity transfer is performed on three channels. Results show that the proposed solutions can effectively improve the contrast and illuminance, and good visual perception for quality degraded retinal images is obtained. Illustration of contrast stretching based on a signal colour channel. [ABSTRACT FROM AUTHOR]

Registration of retinal images is significant for clinical diagnosis. Numerous methods have been proposed to evaluate registration performance. The available evaluation methods can work well in normal image pairs, but fair evaluation cannot be obtained for image pairs with anatomical changes. We propose an automatic method to quantitatively assess the registration of retinal images based on the extraction of similar vessel structures and modified Hausdorff distance. Firstly, vessel detection and skeletonization are performed to detect the vascular centerline. Secondly, the vessel segments having similar structures in the image pair are selected for assessment of registration. The bifurcation and terminal points are determined from the vascular centerline. Then, the Hungarian matching algorithm with a pruning process is employed to match the bifurcation and terminal points to detect similar vessel segments. Finally, a modified Hausdorff distance is employed to evaluate the performance of registration. Our experimental results show that the Pearson product-moment correlation coefficient can reach 0.76 and 0.63 in test set of normal image pairs and image pairs with anomalies respectively, which outperforms other methods. An accurate evaluation can not only compare the performance of different registration methods but also can facilitate the clinical diagnosis by screening out the inaccurate registration. Graphical abstract . [ABSTRACT FROM AUTHOR]

Normally, the optic disc detection of retinal images is useful during the treatment of glaucoma and diabetic retinopathy. In this paper, the novel preprocessing of a retinal image with a bat algorithm (BA) optimization is proposed to detect the optic disc of the retinal image. As the optic disk is a bright area and the vessels that emerge from it are dark, these facts lead to the selected segments being regions with a great diversity of intensity, which does not usually happen in pathological regions. First, in the preprocessing stage, the image is fully converted into a gray image using a gray scale conversion, and then morphological operations are implemented in order to remove dark elements such as blood vessels, from the images. In the next stage, a bat algorithm (BA) is used to find the optimum threshold value for the optic disc location. In order to improve the accuracy and to obtain the best result for the segmented optic disc, the ellipse fitting approach was used in the last stage to enhance and smooth the segmented optic disc boundary region. The ellipse fitting is carried out using the least square distance approach. The efficiency of the proposed method was tested on six publicly available datasets, MESSIDOR, DRIVE, DIARETDB1, DIARETDB0, STARE, and DRIONS-DB. The optic disc segmentation average overlaps and accuracy was in the range of 78.5-88.2% and 96.6-99.91% in these six databases. The optic disk of the retinal images was segmented in less than 2.1 s per image. The use of the proposed method improved the optic disc segmentation results for healthy and pathological retinal images in a low computation time. Graphical abstract ᅟ. [ABSTRACT FROM AUTHOR]