Blood vessel extraction of diabetic retinopathy using optimized enhanced images and matched filter

An efficient registration-based approach for retinal blood vessel segmentation using generalized Pareto and fatigue pdf

Segmentation of Retinal Blood Vessel (RBV) extraction in the retina images and Registration of segmented RBV structure is implemented to identify changes in vessel structure by ophthalmologists in diagnosis of various illnesses like Glaucoma, Diabetes, and Hypertension's. The Retinal Blood Vessel provides blood to the inner retinal neurons, RBV are located mainly in internal retina but it may partly in the ganglion cell layer, following network failure haven't been identified with past methods. Classifications of accurate RBV and Registration of segmented blood vessels are challenging tasks in the low intensity background of Retinal Image. So, we projected a novel approach of segmentation of RBV extraction used matched filter of Generalized Pareto Probability Distribution Function (pdf) and Registration approach on feature-based segmented retinal blood vessel of Binary Robust Invariant Scalable Key point (BRISK). The BRISK provides the predefined sampling pattern as compared to Pdf. The BRISK feature is implemented for attention point recognition & matching approach for change in vessel structure. The proposed approaches contain 3 levels: pre-processing, matched filter-based Generalized Pareto pdf as a source along with the novel approach of fatigue pdf as a target, and BRISK framework is used for Registration on segmented retinal images of supply & intention images. This implemented system's performance is estimated in experimental analysis by the Average accuracy, Normalized Cross-Correlation (NCC), and computation time process of the segmented retinal source and target image. The NCC is main element to give more statistical information about retinal image segmentation. The proposed approach of Generalized Pareto value pdf has Average Accuracy of 95.21%, NCC of both image pairs is 93%, and Average accuracy of Registration of segmented source images and the target image is 98.51% respectively. The proposed approach of average computational time taken is around 1.4 s, which has been identified on boundary condition of Pdf function.

Guidance Image-Based Enhanced Matched Filter with Modified Thresholding for Blood Vessel Extraction

Fundus images have been established as an important factor in analyzing and recognizing many cardiovascular and ophthalmological diseases. Consequently, precise segmentation of blood using computer vision is vital in the recognition of ailments. Although clinicians have adopted computer-aided diagnostics (CAD) in day-to-day diagnosis, it is still quite difficult to conduct fully automated analysis based exclusively on information contained in fundus images. In fundus image applications, one of the methods for conducting an automatic analysis is to ascertain symmetry/asymmetry details from corresponding areas of the retina and investigate their association with positive clinical findings. In the field of diabetic retinopathy, matched filters have been shown to be an established technique for vessel extraction. However, there is reduced efficiency in matched filters due to noisy images. In this work, a joint model of a fast guided filter and a matched filter is suggested for enhancing abnormal retinal images containing low vessel contrasts. Extracting all information from an image correctly is one of the important factors in the process of image enhancement. A guided filter has an excellent property in edge-preserving, but still tends to suffer from halo artifacts near the edges. Fast guided filtering is a technique that subsamples the filtering input image and the guidance image and calculates the local linear coefficients for upsampling. In short, the proposed technique applies a fast guided filter and a matched filter for attaining improved performance measures for vessel extraction. The recommended technique was assessed on DRIVE and CHASE_DB1 datasets and achieved accuracies of 0.9613 and 0.960, respectively, both of which are higher than the accuracy of the original matched filter and other suggested vessel segmentation algorithms.

Dorsal Hand Vein Image Enhancement Using Fusion of CLAHE and Fuzzy Adaptive Gamma

Enhancement of captured hand vein images is essential for a number of purposes, such as accurate biometric identification and ease of medical intravenous access. This paper presents an improved hand vein image enhancement technique based on weighted average fusion of contrast limited adaptive histogram equalization (CLAHE) and fuzzy adaptive gamma (FAG). The proposed technique is applied using three stages. Firstly, grey level intensities with CLAHE are locally applied to image pixels for contrast enhancement. Secondly, the grey level intensities are then globally transformed into membership planes and modified with FAG operator for the same purposes. Finally, the resultant images from CLAHE and FAG are fused using improved weighted averaging methods for clearer vein patterns. Then, matched filter with first-order derivative Gaussian (MF-FODG) is employed to segment vein patterns. The proposed technique was tested on self-acquired dorsal hand vein images as well as images from the SUAS databases. The performance of the proposed technique is compared with various other image enhancement techniques based on mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM). The proposed enhancement technique’s impact on the segmentation process has also been evaluated using sensitivity, accuracy, and dice coefficient. The experimental results show that the proposed enhancement technique can significantly enhance the hand vein patterns and improve the detection of dorsal hand veins.

Convolutional neural-network-based classification of retinal images with different combinations of filtering techniques

Retinal image analysis is one of the important diagnosis methods in modern ophthalmology because eye information is present in the retina. The image acquisition process may have some effects and can affect the quality of the image. This can be improved by better image enhancement techniques combined with the computer-aided diagnosis system. Deep learning is one of the important computational application techniques used for a medical imaging application. The main aim of this article is to find the best enhancement techniques for the identification of diabetic retinopathy (DR) and are tested with the commonly used deep learning techniques, and the performances are measured. In this article, the input image is taken from the Indian-based database named as Indian Diabetic Retinopathy Image Dataset, and 13 filters are used including smoothing and sharpening filters for enhancing the images. Then, the quality of the enhancement techniques is compared using performance metrics and better results are obtained for Median, Gaussian, Bilateral, Wiener, and partial differential equation filters and are combined for improving the enhancement of images. The output images from all the enhanced filters are given as the convolutional neural network input and the results are compared to find the better enhancement method.

A review of machine learning methods for retinal blood vessel segmentation and artery/vein classification

The eye affords a unique opportunity to inspect a rich part of the human microvasculature non-invasively via retinal imaging. Retinal blood vessel segmentation and classification are prime steps for the diagnosis and risk assessment of microvascular and systemic diseases. A high volume of techniques based on deep learning have been published in recent years. In this context, we review 158 papers published between 2012 and 2020, focussing on methods based on machine and deep learning (DL) for automatic vessel segmentation and classification for fundus camera images. We divide the methods into various classes by task (segmentation or artery-vein classification), technique (supervised or unsupervised, deep and non-deep learning, hand-crafted methods) and more specific algorithms (e.g. multiscale, morphology). We discuss advantages and limitations, and include tables summarising results at-a-glance. Finally, we attempt to assess the quantitative merit of DL methods in terms of accuracy improvement compared to other methods. The results allow us to offer our views on the outlook for vessel segmentation and classification for fundus camera images.

Novel Classification of Early-stage Systemic Hypertensive Changes in Human Retina Based on OCTA Measurement of Choriocapillaris

The traditional classification of hypertensive retinopathy was based on the Keith-Wagener-Barker (KWB) grading, which is a subjective scaling system, and it is difficult to distinguish between the first and second grades. Retinal and choroidal vasculatures are affected by systemic hypertension, although retinal vasculature changes with age, axial length, intraocular pressure, and retinal diseases. It is necessary to establish a new objective method to assess hypertensive vascular changes. In the present study, we have examined the vasculature of the macular choriocapillaris in order to establish a new objective method to assess hypertensive vascular changes using optical coherence tomography angiography (OCTA). Choriocapillaris vessel density (VD), vessel length, and vessel diameter index in a 3 × 3 mm macular area were measured by OTCA in a total of 567 volunteers (361 healthy subjects and 206 subjects with systemic hypertension) who attended a basic health check-up. Ocular factors, systemic factors, and medications were evaluated. We detected significant differences in normative choriocapillaris vasculature between the left and right eyes in 53 healthy subjects and revealed correlations between age, intraocular pressure, axial length, and choriocapillaris vasculature in 308 healthy subjects. Normative foveal VD was correlated with age only and the efficiency was weak. The analysis of 206 right eyes (KWB grade 0, 159 eyes; grade 1, 35 eyes; and grade 2, 12 eyes) revealed that foveal VD was strongly correlated with KWB grade only (P < 0.001). This is the first report suggesting that OCTA for foveal choriocapillaris measurement by OCTA would might provide the advantage of evaluating be objective method for evaluating the progression of systemic hypertension.

Feature Selection and Parameters Optimization of Support Vector Machines Based on Hybrid Glowworm Swarm Optimization for Classification of Diabetic Retinopathy

Diabetic Retinopathy (DR) has been a leading cause of blindness in case of human beings falling between the ages of 20 and 74 years. This will have a major influence on both the patient and the society as it can normally influence the humans in their gainful years. An early DR detection is quite challenging as it may not be detected by humans. There are several techniques and algorithms that have been established for detecting the DR. These techniques have been facing problems to achieve effective sensitivity, accuracy, and specificity. In order to overcome all these problems, the work has proposed one more such effective algorithm for image processing in order to increase the efficiency and also identify easily the DR diseases. A major challenge in the task is the automatic detection of the microaneurysms. In this work, the Support Vector Machine (SVM) parameters optimized with Glowworm Swarm Optimization (GSO) and Genetic Algorithm (GA) is used to classify the DR. Because the SVM parameter C and γ to control the performance of the classifier. For this work, the SVMs get fused with the hybrid GSO-GA along with the feature chromosomes that are generated that will thereby direct the GA search to a straight line of the error of optimal generalization in their super parameter space. This GSO algorithm will not have memory and the glow worms will not retain any information in memory. The results of the experiment prove that this method had achieved a better performance.

Evolutionary Computing Enriched Computer-Aided Diagnosis System for Diabetic Retinopathy: A Survey

Complications caused due to diabetes mellitus result in significant microvasculature that eventually causes diabetic retinopathy (DR) that keeps on increasing with time, and eventually causes complete vision loss. Identifying subtle variations in morphological changes in retinal blood vessels, optic disk, exudates, microaneurysms, hemorrhage, etc., is complicated and requires a robust computer-aided diagnosis (CAD) system so as to enable earlier and efficient DR diagnosis practices. In the majority of the existing CAD systems, functional enhancements have been realized time and again to ensure accurate and efficient diagnosis of DR. In this survey paper, a number of existing literature presenting DR CAD systems are discussed and analyzed. Both traditional and varoius evolutionary approaches, including genetic algorithm, particle swarm optimization, ant colony optimization, bee colony optimization, etc., based DR CAD have also been studied and their respective efficiencies have been discussed. Our survey revealed that evolutionary computing methods can play a vital role for optimizing DR-CAD functional components, such as proprocessing by enhancing filters coefficient, segmentation by enriching clustering, feature extraction, feature selection, and dimensional reduction, as well as classification.