Blood vessel segmentation in retinal fundus images using Gabor filters, fractional derivatives, and Expectation Maximization

Aguirre–Ramos, Hugo; Aviña-Cervantes, Juan Gabriel; Cruz-Aceves, Iván; Ruiz-Pinales, José; Ledesma, Sergio

doi:10.1016/j.amc.2018.07.057

Cited by 53 publications

(24 citation statements)

References 41 publications

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“…In 2018, Ramos et al 27 had described the agenda to identify the blood vessels present in retinal images. The noise existing in the green channels of the RGB image was reduced.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Yet, it has some conflicts like requiring high‐performance hardware, and it is extremely expensive to train due to complex data models. Thresholding classification 27 exhibits good overall performance, and the noise is reduced in the green channel of the RGB image. However, it has some defects, like by utilizing the global threshold approach, the blood vessel pixels are absorbed in the nonblood vessel pixels.…”

Section: Literature Reviewmentioning

confidence: 99%

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Automatic diagnosis of diabetic retinopathy with the aid of adaptive average filtering with optimized deep convolutional neural network

Roshini¹,

Ravi²,

Mathew³

et al. 2020

Int J Imaging Syst Tech

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The most effective treatment for diabetic retinopathy (DR) is the early detection through regular screening, which is critical for a better prognosis. Automatic screening of the images would assist the physicians in diagnosing the condition of patients easily and accurately. This condition searches out for special importance of image processing technology in the way of processing the retinal fundus images. Accordingly, this article plans to develop an automatic DR detection model with the aid of three main stages like (a) image preprocessing, (b) blood vessel segmentation, and (c) classification. The preprocessing phase includes two steps: conversion of RGB to Lab, and contrast enhancement. The Histogram equalization process is done using the contrast enhancement of an image. To the next of preprocessing, the segmentation phase starts with a valuable procedure. It includes (a), thresholding the contrast‐enhanced and filtered images, (b) thresholding the keypoints of contrast‐enhanced and filtered images, and (c) adding both thresholded binary images. Here, the filtering process is performed by proposed adaptive average filtering, where the filter coefficients are tuned or optimized by an improved meta‐heuristic algorithm called fitness probability‐based CSO (FP‐CSO). Finally, the classification part uses Deep CNN, where the improvement is exploited on the convolutional layer, which is optimized by the same improved FP‐CSO. Since the conventional CSO depends on a fitness probability in the improved algorithm, the proposed algorithm termed as FP‐CSO. Finally, valuable comparative and performance analysis has confirmed the effectiveness of the proposed model.

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“…In 2018, Ramos et al 27 had described the agenda to identify the blood vessels present in retinal images. The noise existing in the green channels of the RGB image was reduced.…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Automatic diagnosis of diabetic retinopathy with the aid of adaptive average filtering with optimized deep convolutional neural network

Roshini¹,

Ravi²,

Mathew³

et al. 2020

Int J Imaging Syst Tech

View full text Add to dashboard Cite

show abstract

“…Where 1 and 2 show two classes, background, and foreground. The probability for each class and its variance can be calculated by equations (9)(10)(11).…”

Section: Otsu's Thresholdingmentioning

confidence: 99%

“…Another thing is that the performance produced on the frangibased filter segmentation that has been carried out has higher specificity performance parameters than sensitivity, but with a big difference. A number of studies with such performance are carried out by [2], [4], [9], [11]- [17]. Another study is that sensitivity performance parameters are higher than specificity, but with high differences as well, as done by [3], [18].…”

Section: Introductionmentioning

confidence: 99%

Blood Vessels Segmentation in Retinal Fundus Image using Hybrid Method of Frangi Filter, Otsu Thresholding and Morphology

Wiharto¹,

Palgunadi²

2019

IJACSA

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Diagnosis of computer-based retinopathic hypertension is done by analyzing of retinal images. The analysis is carried out through various stages, one of which is blood vessel segmentation in retinal images. Vascular segmentation of the retina is a complex problem. This is caused by non-uniform lighting, contrast variations and the presence of abnormalities due to disease. This makes segmentation not successful if it only relies on one method. The aims of this study to segment blood vessels in retinal images. The method used is divided into three stages, namely preprocessing, segmentation and testing. The first stage, preprocessing, is to improve image quality with the CLAHE method and the median filter on the green channel image. The second stage, segmenting using a number of methods, namely, frangi filter, 2D-convolution filtering, median filtering, otsu's thresholding, morphology operation, and background subtraction. The last step is testing the system using the DRIVE and STARE dataset. The test results obtained sensitivity 91.187% performance parameters, 86.896% specificity, and area under the curve (AUC) 89.041%. Referring to the performance produced, the proposed model can be used as an alternative for blood vessel segmentation of retinal images.

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“…Finally, they have supported their theoretical aspects with a numerical example. In addition to these studies, in the literature there are many papers demonstrate to model other epidemic models and special diseases such as modelling the spread of computer virus [9], stability analysis of a fractional human African trypanosomiasis model [10], a schistosomiasis disease model [11], a novel framework for blood vessels detection in retinal images [12], a parabolic fractional degenerate problem emerging in a spatial diffusion of biological population model [13], SIRI epidemic model with distributed delay and relapse [14], etc.…”

Section: Introductionmentioning

confidence: 99%

Analysis of an Epidemic Spreading Model with Exponential Decay Law

Yavuz¹,

Özdemir²

2020

Mathematical Sciences and Applications E-Notes

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Mathematical modeling of infectious diseases has shown that combinations of isolation, quarantine, vaccine, and treatment are often necessary in order to eliminate most infectious diseases. Continuous mathematical models have been used to study the dynamics of infectious diseases within a human host and in the population. We have used in this study a SIR model that categorizes individuals in a population as susceptible (S), infected (I) and recovered (R). It also simulates the transmission dynamics of diseases where individuals acquire permanent immunity. We have considered the SIR model using the Caputo-Fabrizio and we have obtained special solutions and numerical simulations using an iterative scheme with Laplace transform. Moreover, we have studied the uniqueness and existence of the solutions.

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Blood vessel segmentation in retinal fundus images using Gabor filters, fractional derivatives, and Expectation Maximization

Cited by 53 publications

References 41 publications

Automatic diagnosis of diabetic retinopathy with the aid of adaptive average filtering with optimized deep convolutional neural network

Automatic diagnosis of diabetic retinopathy with the aid of adaptive average filtering with optimized deep convolutional neural network

Blood Vessels Segmentation in Retinal Fundus Image using Hybrid Method of Frangi Filter, Otsu Thresholding and Morphology

Analysis of an Epidemic Spreading Model with Exponential Decay Law

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