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blood vessel segmentation with Classification Model for Diabetic Retinopathy Screening

Computers, Materials & Continua 2023年第4期75卷 2265-2281页

作者： Abdullah O.Alamoudi Sarah Mohammed Allabun Department of Radiological Sciences and Medical Imaging College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Medical Education College of MedicinePrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

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

关键词： Diabetic retinopathy deep learning blood vessel segmentation metaheuristics image processing messidor dataset

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Intelligent Machine Learning Enabled Retinal blood vessel segmentation and Classification

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Computers, Materials & Continua 2023年第1期74卷 399-414页

作者： Nora Abdullah Alkhaldi Hanan T.Halawani Department of Computer Science College of Computer Science and Information TechnologyKing Faisal UniversityP.O.Box 400AlAhsa31982Saudi Arabia College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia

Automated segmentation of blood vessels in retinal fundus images is essential for medical image *** segmentation of retinal vessels is assumed to be essential to the progress of the decision support system for initial analysis and treatment of retinal *** article develops a new Grasshopper Optimization with Fuzzy Edge Detection based Retinal blood vessel segmentation and Classification(GOFED-RBVSC)*** proposed GOFED-RBVSC model initially employs contrast enhancement ***,GOAFED approach is employed to detect the edges in the retinal fundus images in which the use of GOA adjusts the membership *** ORB(Oriented FAST and Rotated BRIEF)feature extractor is exploited to generate feature ***,Improved Conditional Variational Auto Encoder(ICAVE)is utilized for retinal image classification,shows the novelty of 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.

关键词： Edge detection blood vessel segmentation retinal fundus images image classification deep learning

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Automated retinal blood vessels segmentation based on simplified PCNN and fast 2D-Otsu algorithm

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Journal of Central South University 2009年第4期16卷 640-646页

作者：姚畅陈后金 School of Electronics and Information Engineering Beijing Jiaotong University

According to the characteristics of dynamic firing in pulse coupled neural network (PCNN) and regional configuration in retinal blood vessel network, a new method combined with simplified PCNN and fast 2D-Otsu algorithm was proposed for automated retinal blood vessels segmentation. Firstly, 2D Gaussian matched filter was used to enhance the retinal images and simplified PCNN was employed to segment the blood vessels by firing neighborhood neurons. Then, fast 2D-Otsu algorithm was introduced to search the best segmentation results and iteration times with less computation time. Finally, the whole vessel network was obtained via analyzing the regional connectivity. Experiments implemented on the public Hoover database indicate that this new method gets a 0.803 5 true positive rate and a 0.028 0 false positive rate on an average. According to the test results, compared with Hoover algorithm and method of PCNN and 1D-Otsu, the proposed method shows much better performance.

关键词： blood vessel segmentation pulse coupled neural network (PCNN) Otsu neuron

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Spatial continuity incorporated multi-attribute fuzzy clustering algorithm for blood vessels segmentation

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Science China(Information Sciences) 2010年第4期53卷 752-759页

作者： HAO JuTao1*, ZHAO JingJing2 & LI MingLu3 1School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China 2Department of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China 3Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai 200240, China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China Department of Electrical Engineering Shanghai University of Electric Power Shanghai China Department of Computer Science and Engineering Shanghai Jiaotong University Shanghai China

A three-dimensional representation of vasculature can be extremely important in image-guided neurosurgery, pre-surgical planning. In this paper, a spatial continuity incorporated multi-attribute fuzzy clustering algorithm (MAFCM S) is proposed to segment entire blood vessels from TOF MRA images. This clustering method takes both the intensity information and the geometrical information into account, while most of the cur- rent clustering methods only deal with the former. In this method, a new dissimilarity measure, which integrates the intensity and the geometry shape dissimilarity, is introduced. Because of the presence of the geometrical information, the new measure is able to differentiate the pixels with similar intensity values within different geometrical shape structures. Experimental results show that the new algorithm can get better segmentation.

关键词： fuzzy c-means clustering scale space analysis spatial continuity blood vessel segmentation multi-attribute fuzzy clustering

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An Approach to Retinal Image segmentations Using Fuzzy Clustering in Combination with Morphological Filters

An Approach to Retinal Image Segmentations Using Fuzzy Clust...

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第三十届中国控制会议

作者： DING Liang~(1,2),ZHANG Yongping~1,ZHANG Xueying~2 1 School of Electronic and Information Engineering,Ningbo University of Technology,Ningbo 315016,P.R.China 2 College of Information Engineering,Taiyuan University of Technology,Taiyuan,P.R.China

＜正＞Retinal vessel appearance is an important feature for personal identification,information security and confidentiality. It also is a key indicator for many early diagnoses,such as diabetes and *** this approach,a extraction method of retinal vessels based on fuzzy clustering in combination with morphological filtering is *** decomposing the green channel image into smooth and textured components,fuzzy clustering is firstly performed on the textured composite,then the morphological open operation with multiscale linear-like structure elements is applied to suppressing noise structures. Experimental results indicate that the method can automatically and effectively extract most of the vessel backbones and branches.

关键词： blood vessel segmentation Image Decomposition Fuzzy Clustering Morphology Filtering

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