Retinal vessel segmentation based on Frangi filter and morphological reconstruction

Nugroho, Hanung Adi; Aras, Rezty Amalia; Lestari, Tri; Ardiyanto, Igi

doi:10.1109/iccerec.2017.8226686

Cited by 9 publications

(5 citation statements)

References 6 publications

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“…The results of these studies provide relatively the same performance. Similar research was also carried out by Nugroho et al [9]. The research of Nugroho et al [9] has proposed a segmentation www.ijacsa.thesai.org method that combines frangi filters with morphological reconstruction.…”

Section: Introductionmentioning

confidence: 64%

“…Similar research was also carried out by Nugroho et al [9]. The research of Nugroho et al [9] has proposed a segmentation www.ijacsa.thesai.org method that combines frangi filters with morphological reconstruction. Tests performed using the DRIVE dataset show sensitivity of 72.13%, specificity of 96.65% and accuracy of 94.5%.…”

Section: Introductionmentioning

confidence: 64%

“…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%

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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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Section: Introductionmentioning

confidence: 64%

Section: Introductionmentioning

confidence: 64%

“…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%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…Unsupervised approaches are frequently based on the Hessian matrix or Frangi [ 17 , 18 ]. However, they can also use mathematical morphology [ 5 , 19 ], region growing, Fourier transform [ 20 ], etc.…”

Section: Literature Reviewmentioning

confidence: 99%

Local-Sensitive Connectivity Filter (LS-CF): A Post-Processing Unsupervised Improvement of the Frangi, Hessian and Vesselness Filters for Multimodal Vessel Segmentation

Rodrigues

Machado

et al. 2022

J. Imaging

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A retinal vessel analysis is a procedure that can be used as an assessment of risks to the eye. This work proposes an unsupervised multimodal approach that improves the response of the Frangi filter, enabling automatic vessel segmentation. We propose a filter that computes pixel-level vessel continuity while introducing a local tolerance heuristic to fill in vessel discontinuities produced by the Frangi response. This proposal, called the local-sensitive connectivity filter (LS-CF), is compared against a naive connectivity filter to the baseline thresholded Frangi filter response and to the naive connectivity filter response in combination with the morphological closing and to the current approaches in the literature. The proposal was able to achieve competitive results in a variety of multimodal datasets. It was robust enough to outperform all the state-of-the-art approaches in the literature for the OSIRIX angiographic dataset in terms of accuracy and 4 out of 5 works in the case of the IOSTAR dataset while also outperforming several works in the case of the DRIVE and STARE datasets and 6 out of 10 in the CHASE-DB dataset. For the CHASE-DB, it also outperformed all the state-of-the-art unsupervised methods.

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Automated segmentation and quantitative analysis of optic disc and fovea in fundus images

Kim

Lee

Kim

2021

Multimed Tools Appl

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Fundus image is widely used diagnosis method and involves the retinal tissues which can be important biomarkers for diagnosing diseases. Many studies have proposed automatic algorithms to detect the optic disc (OD) and fovea. However, they showed some limitations. Although the precise regions of retinal tissues are clinically important, most of these studies focused on the localization not the segmentation. Also, they did not sufficiently prove the clinical effectiveness of the methods using quantitative analysis. Furthermore, many of them have researched about the single retinal tissue. To compensate for these limitations, this study proposed automated segmentation method for both of the OD and fovea. In this study, the dataset was acquired from the DRIVE and Drions databases, and additional ground truth dataset was obtained from an ophthalmologist. The original fundus image was preprocessed to remove noise and enhance contrast. And the retinal vessel was segmented to use for the OD and fovea segmentation. In the OD and fovea segmentation step, a region of interest was designated based on the image features to increase the segmentation accuracy. To segment the OD, the retinal vessel was removed and substituted based on the intensity value of the four nearest non-vessel pixels. Finally, the OD and fovea regions were segmented based on the image features including intensity, shape and size. The proposed method was evaluated by quantitative analysis using eight methods. As a result, the proposed method showed high segmentation performance for the OD and fovea with accuracy of 99.18 and 99.80 % on the DRIVE database.

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Retinal vessel segmentation based on Frangi filter and morphological reconstruction

Cited by 9 publications

References 6 publications

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

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

Local-Sensitive Connectivity Filter (LS-CF): A Post-Processing Unsupervised Improvement of the Frangi, Hessian and Vesselness Filters for Multimodal Vessel Segmentation

Automated segmentation and quantitative analysis of optic disc and fovea in fundus images

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