Blood Vessel Segmentation with Classification Model for Diabetic Retinopathy Screening

Biomedical image processing is finding useful in healthcare sector for the investigation,enhancement,and display of images gathered by distinct imaging technologies.Diabetic retinopathy(DR)is an illness caused by diabetes complications and leads to irreversible injury to the retina blood vessels.Retinal vessel segmentation techniques are a basic element of automated retinal disease screening system.In this view,this study presents a novel blood vessel segmentation with deep learning based classification(BVS-DLC)model forDRdiagnosis using retinal fundus images.The proposed BVS-DLC model involves different stages of operations such as preprocessing,segmentation,feature extraction,and classification.Primarily,the proposed model uses the median filtering(MF)technique to remove the noise that exists in the image.In addition,a multilevel thresholding based blood vessel segmentation process using seagull optimization(SGO)with Kapur’s entropy is performed.Moreover,the shark optimization algorithm(SOA)with Capsule Networks(CapsNet)model with softmax layer is employed for DR detection and classification.Awide range of simulations was performed on the MESSIDOR dataset and the results are investigated interms of different measures.The simulation results ensured the better performance of the proposed model compared to other existing techniques interms of sensitivity,specificity,receiver operating characteristic(ROC)curve,accuracy,and F-score.

Intelligent Machine Learning Enabled Retinal Blood Vessel Segmentation and Classification

Automated segmentation of blood vessels in retinal fundus images is essential for medical image analysis.The segmentation of retinal vessels is assumed to be essential to the progress of the decision support system for initial analysis and treatment of retinal disease.This article develops a new Grasshopper Optimization with Fuzzy Edge Detection based Retinal Blood Vessel Segmentation and Classification(GOFED-RBVSC)model.The proposed GOFED-RBVSC model initially employs contrast enhancement process.Besides,GOAFED approach is employed to detect the edges in the retinal fundus images in which the use of GOA adjusts the membership functions.The ORB(Oriented FAST and Rotated BRIEF)feature extractor is exploited to generate feature vectors.Finally,Improved Conditional Variational Auto Encoder(ICAVE)is utilized for retinal image classification,shows the novelty of the work.The performance validation of the GOFEDRBVSC model is tested using benchmark dataset,and the comparative study highlighted the betterment of the GOFED-RBVSC model over the recent approaches.

A fast method to detect blood vessel in retinal image

Due to the increasing number ot diabetic patients, the number of people affected by diabetic retinopathy isexpected to increase. Diabetic retinopathy is a complication of diabetes and the most serious frequent eye disease in the world. Large-scale retinal screening for diabetic patients is necessary to prevent visual loss and blindness. The analysis of digital retinal images, obtained by the fundus camera, is viewed as a feasible approach because retinal blood vessels have been shown to change in diameter, branching angles, or tortuosity as a result of diabetic retinopathy. The morphological change can help identify the different stages of diabetic retinopathy. In addition, the acquisition of retinal image is nonintrusive and low cost. Automatic segmentation of the retinal blood vessel is a prerequisite for this analysis.~3 This article presents a method to detect blood vessel based on sobel operators.4 Small and fast computation is the outstanding merit of this method.