A novel framework for retinal vessel segmentation using optimal improved frangi filter and adaptive weighted spatial FCM

Mahapatra, Sakambhari; Agrawal, Sanjay; Mishro, Pranaba K.; Pachori, Ram Bilas

doi:10.1016/j.compbiomed.2022.105770

Cited by 29 publications

(8 citation statements)

References 36 publications

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“…Graph-based techniques, morphological operations, and machine learning-based approaches to vessel skeletonization have been proposed in recent years. For instance, Mahapatra et al [21] proposed a vessel segmentation method based on an optimal enhanced Frangi filter and ranked spatial fuzzy C-means, then used a graphcut technique for skeletonization, achieving the latest performance in terms of efficiency and accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Vessel Segment Extraction in Retinal Fundus Images Using Retinal Image Analysis and Six Sigma Process Capability Index

Badawi,

Takruri,

ElBadawi

et al. 2023

Mathematics

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Retinal vessel segmentation, skeletonization, and the generation of vessel segments are considered significant steps in any automated system for measuring the vessel biomarkers of several disease diagnoses. Most of the current tortuosity quantification methods rely on precise vascular segmentation and skeletonization of the retinal vessels. Additionally, the existence of a reference dataset for accurate vessel segment images is an essential need for implementing deep learning solutions and an automated system for measuring the vessel biomarkers of several disease diagnoses, especially for optimized quantification of vessel tortuosity or accurate measurement of AV-nicking. This study aimed to present an improved method for skeletonizing and extracting the retinal vessel segments from the 504 images in the AV classification dataset. The study utilized the Six Sigma process capability index, sigma level, and yield to measure the vessels’ tortuosity calculation improvement before and after optimizing the extracted vessels. As a result, the study showed that the sigma level for the vessel segment optimization improved from 2.7 to 4.39, the confirming yield improved from 88 percent to 99.77 percent, and the optimized vessel segments of the AV classification dataset retinal images are available in monochrome and colored formats.

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Section: Literature Reviewmentioning

confidence: 99%

Enhancing Vessel Segment Extraction in Retinal Fundus Images Using Retinal Image Analysis and Six Sigma Process Capability Index

Badawi,

Takruri,

ElBadawi

et al. 2023

Mathematics

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“…This work focuses primarily on unsupervised techniques, as they do not require training data and can be used as a feature in machine learning approaches. The state-of-the-art and most recent unsupervised approaches in the literature are the works of Memari et al [ 25 ], Tavakoli et al [ 26 ] and Mahapatra et al [ 27 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Tavakoli et al [ 26 ] used Radon transform and mathematical morphology. Mahapatra et al [ 27 ] also used the Frangi filter, associated to a clustering algorithm. The Frangi filter is prominent in the state of the art when it comes to unsupervised techniques (and also in supervised cases, such as in [ 4 ]).…”

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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“…Such as fuzzy C-means [11] is employed to segment retinal blood vessels. Mahapatra et al [37] proposed a novel framework for retinal vessel segmentation using an optimal improved frangi filter and adaptive weighted spatial fuzzy C-means. Zhang et al [38] used an unsupervised texton dictionary, where vessel textons were derived from the responses of a multiscale Gabor filter bank.…”

Section: Retinal Vessel Segmentationmentioning

confidence: 99%

SFA-Net: Scale and Feature Aggregate Network for Retinal Vessel Segmentation

Liu

et al. 2022

Journal of Healthcare Engineering

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A U-Net-based network has achieved competitive performance in retinal vessel segmentation. Previous work has focused on using multilevel high-level features to improve segmentation accuracy but has ignored the importance of shallow-level features. In addition, multiple upsampling and convolution operations may destroy the semantic feature information contained in the decoder layer. To address these problems, we propose a scale and feature aggregate network (SFA-Net), which can make full use of multiscale high-level feature information and shallow features. In this paper, a residual atrous spatial feature aggregate block (RASF) is embedded at the end of the encoder to learn multiscale information. Furthermore, an attentional feature module (AFF) is proposed to enhance the effective fusion between shallow and high-level features. In addition, we designed the multi-path feature fusion (MPF) block to fuse high-level features of different decoder layers, which aims to learn the relationship between the high-level features of different paths and alleviate the information loss. We apply the network to the three benchmark datasets (DRIVE, STARE, and CHASE_DB1) and compare them with the other current state-of-the-art methods. The experimental results demonstrated that the proposed SFA-Net performs effectively, indicating that the network is suitable for processing some complex medical images.

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A novel framework for retinal vessel segmentation using optimal improved frangi filter and adaptive weighted spatial FCM

Cited by 29 publications

References 36 publications

Enhancing Vessel Segment Extraction in Retinal Fundus Images Using Retinal Image Analysis and Six Sigma Process Capability Index

Enhancing Vessel Segment Extraction in Retinal Fundus Images Using Retinal Image Analysis and Six Sigma Process Capability Index

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

SFA-Net: Scale and Feature Aggregate Network for Retinal Vessel Segmentation

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