Unsupervised Retinal Blood Vessel Segmentation Technique using pdAPSO and Difference Image Methods for Detection of Diabetic Retinopathy

Retinal vessel segmentation is a practice that has the potential of enhancing accuracy in the diagnosis and timely prevention of illnesses that are related to blood vessels. Acute damage to the retinal vessel has been identified to be the main cause of blindness and impaired vision. A timely detection and control of these illnesses can greatly decrease the number of loss of sight cases. However, the manual protocol for such detection is laborious and although autonomous methods have been recommended, the accuracy of these methods is often unreliable. We propose the utilization of the Primal-Dual Asynchronous Particle Swarm Optimisation (pdAPSO) and differential image methods in addressing the drawbacks associated with segmentation of retinal vessels in this study. The fusion of pdAPSO and differential image (which focuses on the median filter) produced a significant enhancement in the segmentation of huge and miniscule retinal vessels. In addition, the method also decreased erroneous detection near the edge of the retinal (that is not sensitive to light). The results are favourable for the median filter when compared to mean filter and Gaussian filter. The accuracy rate of 0.9559 (with a specificity of sensitivity rate of 0.9855), and a sensitivity rate of 0.7218 were obtained when tested using the Digital Retinal Images for Vessel Extraction database. The above result is a pointer that our approach will help in detecting and diagnosing the damage done to the retinal and thereby preventing loss of sight

Multi-Population Differential Evolution for Retinal Blood Vessel Segmentation

The retinal blood vessel segmentation plays a significant role in the automatic or computer-assisted diagnosis of retinopathy. Manual blood vessel segmentation is very time-consuming and requires a great amount of domain knowledge. In addition, the blood vessels are only a few pixels wide and cover the entire fundus image. This further hinders the recent systems from automating the retinal blood vessel segmentation efficiently. In this paper, we propose a modified differential evolution (DE) algorithm to carry out automatic retinal blood vessel segmentation. The modified DE employs cross-communication among multiple populations to select three types of features i.e. thick blood vessels, thin blood vessels and non-blood vessels. Multiple classifiers such as neural networks (NN), Support vector machines (SVM), NN based and SVM based ensembles are used to further measure the performance of segmentation. The proposed algorithm is evaluated on three publicly available retinal image datasets like DRIVE, STARE and HRF. It outperformed the state-of-the-art with a high average accuracy of 98.5% along with high sensitivity and specificity

Modelling on-demand preprocessing framework towards practical approach in clinical analysis of diabetic retinopathy

Diabetic retinopathy (DR) refers to a complication of diabetes and a prime cause of vision loss in middle-aged people. A timely screening and diagnosis process can reduce the risk of blindness. Fundus imaging is mainly preferred in the clinical analysis of DR. However; the raw fundus images are usually subjected to artifacts, noise, low and varied contrast, which is very hard to process by human visual systems and automated systems. In the existing literature, many solutions are given to enhance the fundus image. However, such approaches are particular and limited to a specific objective that cannot address multiple fundus images. This paper has presented an on-demand preprocessing frame work that integrates different techniques to address geometrical issues, random noises, and comprehensive contrast enhancement solutions. The performance of each preprocessing process is evaluated against peak signal-to-noise ratio (PSNR), and brightness is quantified in the enhanced image. The motive of this paper is to offer a flexible approach of preprocessing mechanism that can meet image enhancement needs based on different preprocessing requirements to improve the quality of fundus imaging towards early-stage diabetic retinopathy identification

Hybrid Multilevel Thresholding and Improved Harmony Search Algorithm for Segmentation

This paper proposes a new method for image segmentation is hybrid multilevel thresholding and improved harmony search algorithm. Improved harmony search algorithm which is a method for finding vector solutions by increasing its accuracy. The proposed method looks for a random candidate solution, then its quality is evaluated through the Otsu objective function. Furthermore, the operator continues to evolve the solution candidate circuit until the optimal solution is found. The dataset used in this study is the retina dataset, tongue, lenna, baboon, and cameraman. The experimental results show that this method produces the high performance as seen from peak signal-to-noise ratio analysis (PNSR). The PNSR result for retinal image averaged 40.342 dB while for the average tongue image 35.340 dB. For lenna, baboon and cameramen produce an average of 33.781 dB, 33.499 dB, and 34.869 dB. Furthermore, the process of object recognition and identification is expected to use this method to produce a high degree of accuracy

Intelligent optic disc segmentation using improved particle swarm optimization and evolving ensemble models

In this research, we propose Particle Swarm Optimization (PSO)-enhanced ensemble deep neural networks for optic disc (OD) segmentation using retinal images. An improved PSO algorithm with six search mechanisms to diversify the search process is introduced. It consists of an accelerated super-ellipse action, a refined super-ellipse operation, a modified PSO operation, a random leader-based search operation, an average leader-based search operation and a spherical random walk mechanism for swarm leader enhancement. Owing to the superior segmentation capabilities of Mask R-CNN, transfer learning with a PSO-based hyper-parameter identification method is employed to generate the fine-tuned segmenters for OD segmentation. Specifically, we optimize the learning parameters, which include the learning rate and momentum of the transfer learning process, using the proposed PSO algorithm. To overcome the bias of single networks, an ensemble segmentation model is constructed. It incorporates the results of distinctive base segmenters using a pixel-level majority voting mechanism to generate the final segmentation outcome. The proposed ensemble network is evaluated using the Messidor and Drions data sets and is found to significantly outperform other deep ensemble networks and hybrid ensemble clustering models that are incorporated with both the original and state-of-the-art PSO variants. Additionally, the proposed method statistically outperforms existing studies on OD segmentation and other search methods for solving diverse unimodal and multimodal benchmark optimization functions and the detection of Diabetic Macular Edema

Feature Extraction for Retina Image Based on Difference Approaches

Automatic disease diagnosis using biometric images is a difficult job due to image distortion, such as the presence of artifacts, less or excessive light, narrow vessel visibility and differences in inter-camera variability that affect the performance of an approaches. Almost all extraction methods in the blood vessels in the retina produce the main part of the vessel with no patalogical environment. However, an important problem for this method is that extraction errors occur if they are too focused on the thin vessels, the wide vessels will be more detectable and also artificial vessels that may appear a lot. In addition, when focusing on a wide vessel, the extraction of thin vessels tends to disappear and is low. Based on our research, different treatments are needed to extract thin vessels and wide vessels both visually and in contrast. This study aims to adopt feature extraction strategies with different techniques. The method proposed in segmentation and extraction with three different approaches, namely the pattern of shape, color, and texture. Testing segmentation and feature extraction using STARE datasets with five classes of diseases namely Choroidal Neovascularization, Branch Retinal Vein Occlusion, Histoplasmosis, Myelinated Nerve Fibers, and Coats. Image enhancement on Myelinated Nerve disease Fiber is the best result from the image of other diseases with the highest value of PSNR of 35.4933 dB and the lowest MSE of 0.0003 means that the technique is able to repair objects well. The main significance of this work is to increase the quality of segmentation results by applying the Otsu method. Testing of segmentation results shows improvements with the proposed method compared to other methods. Furthermore, the application of different feature extraction methods will get information on disease classification features based on patterns of suitable shapes, colors, and textures

Automated Systems for Calculating Arteriovenous Ratio in Retinographies : A Scoping Review

There is evidence of an association between hypertension and retinal arteriolar narrowing. Manual measurement of retinal vessels comes with additional variability, which can be eliminated using automated software. This scoping review aims to summarize research on automated retinal vessel analysis systems. Searches were performed on Medline, Scopus, and Cochrane to find studies examining automated systems for the diagnosis of retinal vascular alterations caused by hypertension using the following keywords: diagnosis; diagnostic screening programs; image processing, computer-assisted; artificial intelligence; electronic data processing; hypertensive retinopathy; hypertension; retinal vessels; arteriovenous ratio and retinal image analysis. The searches generated 433 articles. Of these, 25 articles published from 2010 to 2022 were included in the review. The retinographies analyzed were extracted from international databases and real scenarios. Automated systems to detect alterations in the retinal vasculature are being introduced into clinical practice for diagnosis in ophthalmology and other medical specialties due to the association of such changes with various diseases. These systems make the classification of hypertensive retinopathy and cardiovascular risk more reliable. They also make it possible for diagnosis to be performed in primary care, thus optimizing ophthalmological visits