Blood Vessel Detection in Fundus Images Using Frangi Filter Technique

Jothi, Adityan; Jayaram, Shrinivas

doi:10.1007/978-981-10-8971-8_5

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…Different global and adaptive thresholding algorithms have been explored for automated segmentation of vasculature in fundus images and, more recently, in OCTA images, including Otsu's method [15][16][17] and multi-scale vesselness filters (e.g., Frangi filter). [22][23][24][25] However, translation of these thresholding algorithms to grayscale AOSLO perfusion images for the purposes of automatically segmenting retinal vasculature has proven challenging, primarily due to the large variations in contrast, brightness, and background signal that can typically manifest in AOSLO perfusion images. Machine learning techniques, such as convolutional neural networks (CNNs), have been developed for fundus [26][27][28] and OCTA 29 images.…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of Retinal Capillaries in Adaptive Optics Scanning Laser Ophthalmoscope Perfusion Images Using a Convolutional Neural Network

Musial

Queener

Adhikari

et al. 2020

Trans. Vis. Sci. Tech.

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Adaptive optics scanning laser ophthalmoscope (AOSLO) capillary perfusion images can possess large variations in contrast, intensity, and background signal, thereby limiting the use of global or adaptive thresholding techniques for automatic segmentation. We sought to develop an automated approach to segment perfused capillaries in AOSLO images. Methods: 12,979 image patches were extracted from manually segmented AOSLO montages from 14 eyes and used to train a convolutional neural network (CNN) that classified pixels as capillaries, large vessels, background, or image canvas. 1764 patches were extracted from AOSLO montages of four separate subjects, and were segmented manually by two raters (ground truth) and automatically by the CNN, an Otsu's approach, and a Frangi approach. A modified Dice coefficient was created to account for slight spatial differences between the same manually and CNN-segmented capillaries. Results: CNN capillary segmentation had an accuracy (0.94), a Dice coefficient (0.67), and a modified Dice coefficient (0.90) that were significantly higher than other automated approaches (P < 0.05). There were no significant differences in capillary density and mean segment length between manual ground-truth and CNN segmentations (P > 0.05). Conclusions: Close agreement between the CNN and manual segmentations enables robust and objective quantification of perfused capillary metrics. The developed CNN is time and computationally efficient, and distinguishes capillaries from areas containing diffuse background signal and larger underlying vessels. Translational Relevance: This automatic segmentation algorithm greatly increases the efficiency of quantifying AOSLO capillary perfusion images.

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Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of Retinal Capillaries in Adaptive Optics Scanning Laser Ophthalmoscope Perfusion Images Using a Convolutional Neural Network

Musial

Queener

Adhikari

et al. 2020

Trans. Vis. Sci. Tech.

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“…Its principle is slightly difficult and complex to fulfill, although it works great. Another classical comparison method we select in the experiment was proposed by Frangi et al [22] . They speculated on the presentation of vessels in the likelihood by utilizing the eigenvalues of the Hessian.…”

Section: Resultsmentioning

confidence: 99%

FPGA-Based Two-Dimensional Matched Filter Design for Vein Imaging Systems

Xiang

Sun

et al. 2021

IEEE J. Transl. Eng. Health Med.

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Venipuncture is a common medical procedure. The use of augmented reality-based assistive devices can improve the first puncture success rate in patients with poor vascular filling. In order to improve the image rendering quality and speed of auxiliary equipment, this study develop a two-dimensional matched filtering algorithm on a Field Programmable Gate Array (FPGA) in a near-infrared vein imaging system, which use parallel processing to offer real-time response and is designed as a small handheld portable device. A customized dorsal hand vein image library with 200 images captured from 120 participants is used to analyze the effects of convolution kernel parameters and exposure time on vascular imaging with different depths, and the correlation model between these parameters and vascular depth are constructed. We use the Tenengrad, variance, Laplace smoothness and standard deviation as evaluation indicators, and compare our algorithm with three other related studies. Experimental results show that the rendering quality of our proposed algorithm is significantly higher than other algorithms. In addition, the rendering speed of our algorithm can reach 66 fps, which is twice faster than the current fastest algorithm.

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“…Jothi et al [44] have described a new algorithm based on Frangi filter based on the concept that vessels in image consists of a small area to be considered and are lighter or darker than their background. Samuel et al [45] introduced CNN based method Vessel Specific Skip Chain Convolution Network for BV segmentation which is used to detect vessels in fundus as well as X-ray Coronary Angiogram images.…”

Section: Blood Vessel (Bv) Segmentationmentioning

confidence: 99%

A Survey on Automatic Diabetic Retinopathy Screening

Nage

Shitole

2021

SN COMPUT. SCI.

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Diabetes is a chronic disease caused due to the increase in the sugar level in the blood. Diabetes mainly affects heart, blood vessels, kidneys, eyes and nerves. There are mellitus type 1 and 2 diabetes, in which insulin is either not taken by the body or not created by the body. As per the statistics provided by WHO, about 422 million people worldwide are fighting with diabetes. Primary source of vision loss in the patients suffering from diabetes is diabetic retinopathy where the individual suffers from the damage and growth of abnormal blood veins in the retina. The disease is observed by ophthalmologist through identifying the presence of abnormalities starting from microaneurysms in the non-proliferative stage of DR and if these lesions' presence is ignored and not detected then it leads to the neovascularization in the proliferative stage which leads to unavoidable vision loss. DR can be cured if detected at the beginning stage. Manual method takes lots of time for detection of DR hence it is important to develop computer-based diagnostic system for DR detection using artificial intelligence (AI) and advance image processing to help ophthalmologists for spotting early symptoms of DR in less time. This paper provides a descriptive study about recent trends and technologies used for automatic spotting and grading of DR.

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Blood Vessel Detection in Fundus Images Using Frangi Filter Technique

Cited by 3 publications

References 16 publications

Automatic Segmentation of Retinal Capillaries in Adaptive Optics Scanning Laser Ophthalmoscope Perfusion Images Using a Convolutional Neural Network

Automatic Segmentation of Retinal Capillaries in Adaptive Optics Scanning Laser Ophthalmoscope Perfusion Images Using a Convolutional Neural Network

FPGA-Based Two-Dimensional Matched Filter Design for Vein Imaging Systems

A Survey on Automatic Diabetic Retinopathy Screening

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