Retracted: Robust Retinal Vessel Segmentation using Vessel's Location Map and Frangi Enhancement Filter

Shahid, Muhammad; Taj, Imtiaz A.

doi:10.1049/iet-ipr.2017.0457

Cited by 25 publications

(14 citation statements)

References 23 publications

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“…The resulting performance, for parameter sensitivity was 73.0%, specificity was 97.93% and accuracy was 95.8%. The study was similar to that of Shahid & Taj [2], which is a combination of the frangi filter with VLM. The results of these studies provide relatively the same performance.…”

Section: Introductionsupporting

confidence: 67%

“…The next comparison with the research conducted by Khan et al [33] and Shahid & Taj [2]. Both of these studies use a method that combines the frangi filter in segmentation.…”

Section: B Discussionmentioning

confidence: 91%

“…The segmentation method in image processing is divided into 4 classes, namely edge detection, thresholding-based, region-based and clustering-based [1]. Blood vessel segmentation in the retina is relatively difficult because the retinal image produced by the fundus camera has non-uniform lighting and contrast variations [2]. Another thing that causes difficulty in the process of segmentation is the presence of a disease so that an abnormality will emerge which may be similar to a blood vessel or other sign.…”

Section: Introductionmentioning

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.

show abstract

Section: Introductionsupporting

confidence: 67%

“…The next comparison with the research conducted by Khan et al [33] and Shahid & Taj [2]. Both of these studies use a method that combines the frangi filter in segmentation.…”

Section: B Discussionmentioning

confidence: 91%

Section: Introductionmentioning

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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“…The Frangi filter is a multi-scale second-order vessel enhancement method developed by Frangi et al (1998) that is frequently used in biomedical applications (Vazquez et al, 2001;Budai et al, 2013;Shahid and Taj, 2018). The Frangi filter is used for enhancement of high-contrast vessel structures or edges along with the suppression of the non-vessel structures and thin vessel edges.…”

Section: Frangi Filter Enhancementmentioning

confidence: 99%

Automated Spike Detection in Diverse European Wheat Plants Using Textural Features and the Frangi Filter in 2D Greenhouse Images

et al. 2020

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Penumbra quantification from MR SWI‐DWI mismatch and its comparison with MR ASL PWI‐DWI mismatch in patients with acute ischemic stroke

et al. 2021

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In acute-ischemic-stroke patients, penumbra assessment plays a significant role in treatment outcome. MR perfusion-weighted imaging (PWI) and diffusion-weighted imaging (DWI) mismatch ratio can provide penumbra assessment. Recently reported studies have shown the potential of susceptibility-weighted imaging (SWI) in the qualitative assessment of penumbra. We hypothesize that quantitative penumbra assessment using SWI-DWI can provide an alternative to the PWI-DWI approach and this can also reduce the overall scan-time. The purpose of the current study was to develop a framework for accurate quantitative assessment of penumbra using SWI-DWI and its validation with PWI-DWI-based quantification. In the current study, the arterial-spin-labelling (ASL) technique has been used for PWI. This retrospective study included 25 acute-ischemic-stroke patients presenting within 24 hours of the last noted baseline condition of stroke onset. Eleven patients also had follow-up MRI within 48 hours. MRI acquisition comprised DWI, SWI, pseudo-continuous-ASL (pCASL), FLAIR and non-contrast-angiography sequences. A framework was developed for the enhancement of prominent hypo-intense vein signs followed by automatic segmentation of the SWI penumbra ROI. Apparent-diffusion-coefficient (ADC) maps and cerebral-blood-flow (CBF) maps were computed. The infarct core ROI from the ADC map and the ASL penumbra ROI from CBF maps were segmented semiautomatically. The infarct core volume, SWI penumbra volume (SPV) and pCASL penumbra volume were computed and used to calculate mismatch ratios MR SWI ADC

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Retracted: Robust Retinal Vessel Segmentation using Vessel's Location Map and Frangi Enhancement Filter

Cited by 25 publications

References 23 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

Automated Spike Detection in Diverse European Wheat Plants Using Textural Features and the Frangi Filter in 2D Greenhouse Images

Penumbra quantification from MR SWI‐DWI mismatch and its comparison with MR ASL PWI‐DWI mismatch in patients with acute ischemic stroke

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