Retinal vessel segmentation approach based on corrected morphological transformation and fractal dimension

Wang, Weihua; Wang, Weiqing; Hu, Zhangping

doi:10.1049/iet-ipr.2018.5636

Cited by 24 publications

(19 citation statements)

References 43 publications

(63 reference statements)

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“…As shown in Table 4, the accuracy of the proposed method is better than (Sun et al, 2019) and (Wang et al, 2019). Also, the confidence interval of results in the proposed method is narrow than the other two methods.…”

Section: Comparing Dice Similarity Coefficientmentioning

confidence: 89%

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Microscopic retinal blood vessels detection and segmentation using support vector machine and K‐nearest neighbors

Rehman

Harouni

Karimi

et al. 2022

Microscopy Res & Technique

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The retina is the deepest layer of texture covering the rear of the eye, recorded by fundus images. Vessel detection and segmentation are useful in disease diagnosis. The retina's blood vessels could help diagnose maladies such as glaucoma, diabetic retinopathy, and blood pressure. A mix of supervised and unsupervised strategies exists for the detection and segmentation of blood vessels images. The tree structure of retinal blood vessels, their random area, and different thickness have caused vessel detection difficulties at machine learning calculations. Since the green band of retinal images conveys more information about the vessels, they are utilized for microscopic vessels detection. The current research proposes an administered calculation for segmentation of retinal vessels, where two upgrading stages depending on filtering and comparative histogram were applied after pre‐processing and image quality improvement. At that point, statistical features of vessel tracking, maximum curvature and curvelet coefficient are extracted for each pixel. The extracted features are classified by support vector machine and the k‐nearest neighbors. The morphological operators then enhance the classified image at the final stage to segment with higher accuracy. The dice coefficient is utilized for the evaluation of the proposed method. The proposed approach is concluded to be better than different strategies with a normal of 92%.

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Section: Comparing Dice Similarity Coefficientmentioning

confidence: 89%

“…To evaluate the proposed method compared with the (Sun et al, 2019) and (Wang et al, 2019). The results presented in Table 4.…”

Section: Comparing Dice Similarity Coefficientmentioning

confidence: 99%

Microscopic retinal blood vessels detection and segmentation using support vector machine and K‐nearest neighbors

Rehman

Harouni

Karimi

et al. 2022

Microscopy Res & Technique

View full text Add to dashboard Cite

show abstract

“…There is a substantial effort reported in the literature for the segmentation of blood vessels in fundus images [20–42]. A comprehensive review on existing methods in retinal vessel segmentation and available public datasets are presented in [43, 44].…”

Section: Previous Workmentioning

confidence: 99%

“…An improved method for blood vessel segmentation using morphological component analysis was introduced by [42, 49]. Martinez‐Perez et al.…”

Section: Previous Workmentioning

confidence: 99%

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Unsupervised automated retinal vessel segmentation based on Radon line detector and morphological reconstruction

Tavakoli

Mehdizadeh

Shahri

et al. 2021

IET Image Processing

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Retinal blood vessel segmentation and analysis is critical for the computer‐aided diagnosis of different diseases such as diabetic retinopathy. This study presents an automated unsupervised method for segmenting the retinal vasculature based on hybrid methods. The algorithm initially applies a preprocessing step using morphological operators to enhance the vessel tree structure against a non‐uniform image background. The main processing applies the Radon transform to overlapping windows, followed by vessel validation, vessel refinement and vessel reconstruction to achieve the final segmentation. The method was tested on three publicly available datasets and a local database comprising a total of 188 images. Segmentation performance was evaluated using three measures: accuracy, receiver operating characteristic (ROC) analysis, and the structural similarity index. ROC analysis resulted in area under curve values of 97.39%, 97.01%, and 97.12%, for the DRIVE, STARE, and CHASE‐DB1, respectively. Also, the results of accuracy were 0.9688, 0.9646, and 0.9475 for the same datasets. Finally, the average values of structural similarity index were computed for all four datasets, with average values of 0.9650 (DRIVE), 0.9641 (STARE), and 0.9625 (CHASE‐DB1). These results compare with the best published results to date, exceeding their performance for several of the datasets; similar performance is found using accuracy.

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Shell‐Net: A robust deep neural network for the joint segmentation of retinal fragments

Pappu,

Uppudi,

Biswal

et al. 2023

Int J Imaging Syst Tech

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Segmentation of retinal fragments like blood vessels, optic disc (OD), and optic cup (OC) enables the early detection of different retinal pathologies like diabetic retinopathy (DR), glaucoma, etc. This article proposed a novel deep learning architecture termed as Shell‐Net for the accurate segmentation of the retinal fragments. The main novelty of the architecture relies on the intellectual fusion of two different styled networks for attaining better segmentation results. The lower part of the Shell‐Net (feature condenser) follows the down‐sampling and up‐sampling style, whereas the upper part of the network (feature amplifier) follows the up‐sampling and down‐sampling style of architecture. In addition to this, an additional residual module (feature stabilizer) is integrated with the network to achieve more spatial information from lower levels. The lower part of the network reduces the data through summarization, enabling much scope for precise extraction of heavy details such as thick vessels, OD, and OC. On the contrary, the upper part of the network augments the data using duplication, assisting in the enlargement of minuscule details such as the tiny vessels and boundaries. Experiments were performed on publicly available datasets like Digital Retinal Images for Vessel Extraction (DRIVE), Child Heart and Health Study in England (CHASE_DB1), Structured Analysis of the Retina (STARE), Online Retinal Fundus Image Dataset for Glaucoma Analysis and Research (ORIGA), DRISHTI‐GS1, and Retinal Image database for Optic Nerve Evaluation (RIMONE r1). The network accomplished an average accuracy (ACC) and specificity (SPE) of 0.96 and 0.98 when tested on DRIVE, CHASE_DB1, and STARE datasets respectively for vessel segmentation. Furthermore, it outperformed previously existing models in OD and OC segmentation by achieving an average accuracy of 0.98 with a specificity of 0.99 on the DRISHTI_GS1 dataset.

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Retinal vessel segmentation approach based on corrected morphological transformation and fractal dimension

Cited by 24 publications

References 43 publications

Microscopic retinal blood vessels detection and segmentation using support vector machine and K‐nearest neighbors

Microscopic retinal blood vessels detection and segmentation using support vector machine and K‐nearest neighbors

Unsupervised automated retinal vessel segmentation based on Radon line detector and morphological reconstruction

Shell‐Net: A robust deep neural network for the joint segmentation of retinal fragments

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