Retinal blood vessel segmentation from fundus image using an efficient multiscale directional representation technique Bendlets

Kushol, Rafsanjany; Kabir, Md. Hasanul; Abdullah-Al-Wadud, M.; Islam, Saiful

doi:10.3934/mbe.2020394

Cited by 12 publications

(5 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Orujov et al [16] proposed to segment the blood vessels from fundus image through Mandani type-2 fuzzy logic rules. Initially they process the input retinal image for contrast enhancement and [17] employed BT to represent each pixel of retinal image through its directional information and applied an ensemble machine learning algorithm for classification. Next, N. Tamim et al [18] proposed to represent each pixel is represented with 24 features.…”

Section: Literature Surveymentioning

confidence: 99%

See 1 more Smart Citation

Retinal Vasculature Segmentation Based on Morphology and Pixel Level Classification

2023

IJIES

View full text Add to dashboard Cite

Retinal images are found as the major resources for the automatic diagnosis of diabetic retinopathy (DR). However, the advanced stages like proliferative diabetic retinopathy (PDR) cause the branch out of new and thin vessels in the retina. These vessels create a lot of confusion at the diagnosis of DR through retinal mages. Hence, this paper proposes a new blood vessels segmentation mechanism in three stages; they are major blood vessels segmentation, minor blood vessels segmentation and post-processing. In the first stage, the retinal image is enhanced for quality enhancement followed low pass filtering and top-hat transform to segment major blood vessels. In the second stage, the residue image left in the first stage is processed for pixel level classification. At this stage, each pixel is represented with a set of 19 features. Then this feature vector is fed to support vector machine (SVM) for classification. Finally, the resultant images obtained in the first two stages are combined to get the final retinal vessel structure. For simulation, we used two standard retinal image datasets namely STARE and CHASE_DB and the performance is measured through sensitivity, accuracy, specificity, Jaccard Similarity index and Dice similarity index. Simulation results show that the proposed method achieved better segmentation accuracy and it is of approximately 96.85% and 96.66% for STARE and CHASE_DB respectively.

show abstract

Section: Literature Surveymentioning

confidence: 99%

“…The newly emerged vessels are in resemblance with background and they can't be classified perfectly. Some methods like [15] and [17] applied transformation and filtering techniques to segment the vessels structure. However, they had shown limited performance than the pixel based classification methods.…”

Section: Performance Metricsmentioning

confidence: 99%

Retinal Vasculature Segmentation Based on Morphology and Pixel Level Classification

2023

IJIES

View full text Add to dashboard Cite

show abstract

“…They apply various leveled picture tangling strategies to separate the vessel parts in obscure regions. In a paper by R. Kushol et al [12], an efficient retinal blood vessel segmentation approach was proposed by constructing a 4-D feature vector for the output of Bendlet transform, which can capture directional information much more efficiently than the traditional wavelets. The approach is performed on two image datasets (DRIVE and STARE) with an average accuracy for vessel segmentation achieved of approximately 95%.…”

Section: Literature Viewmentioning

confidence: 99%

Blood Vessels Segmentation in Eye Fundus Images Using Image Processing Algorithms

Al-quraan,

Alquran,

Alsalatie

et al. 2023

Proceedings of International Conference on Artificial Life and Robotics

View full text Add to dashboard Cite

The retinal blood vessels have a huge impact on the diagnosis of eye diseases in addition to other systematic diseases in the human body. In this paper, we presented an automated segmentation method to extract retinal blood vessels, starting with preprocessing, then passing the image into segmentation stage using Bradley technique, and lastly, morphological operations. The proposed method was assessed and tested on STARE dataset, followed by comparing the auto-segmented images to the manually segmented ones. The comparison results Accuracy, Sensitivity, and Specificity were 94.63%, 95.02%, and 80.73% respectively.

show abstract

“…However, challenges still persist in clinical scenarios. To address the challenges of uneven illumination, blurring, and various anomalies, in enhancing retinal images, Liu and Huang [29] introduced a combined approach for improving low-quality retinal images and segmenting blood vessels, utilizing a diffusion model for both tasks [30]. Furthermore, Oh et al (2023) [31] introduced a novel retina image enhancement framework using scattering transform.…”

Section: Introductionmentioning

confidence: 99%

Robust PCA with Lw,∗ and L2,1 Norms: A Novel Method for Low-Quality Retinal Image Enhancement

Likassa,

Chen,

Chen

et al. 2024

J. Imaging

View full text Add to dashboard Cite

Nonmydriatic retinal fundus images often suffer from quality issues and artifacts due to ocular or systemic comorbidities, leading to potential inaccuracies in clinical diagnoses. In recent times, deep learning methods have been widely employed to improve retinal image quality. However, these methods often require large datasets and lack robustness in clinical settings. Conversely, the inherent stability and adaptability of traditional unsupervised learning methods, coupled with their reduced reliance on extensive data, render them more suitable for real-world clinical applications, particularly in the limited data context of high noise levels or a significant presence of artifacts. However, existing unsupervised learning methods encounter challenges such as sensitivity to noise and outliers, reliance on assumptions like cluster shapes, and difficulties with scalability and interpretability, particularly when utilized for retinal image enhancement. To tackle these challenges, we propose a novel robust PCA (RPCA) method with low-rank sparse decomposition that also integrates affine transformations τi, weighted nuclear norm, and the L2,1 norms, aiming to overcome existing method limitations and to achieve image quality improvement unseen by these methods. We employ the weighted nuclear norm (Lw,*) to assign weights to singular values to each retinal images and utilize the L2,1 norm to eliminate correlated samples and outliers in the retinal images. Moreover, τi is employed to enhance retinal image alignment, making the new method more robust to variations, outliers, noise, and image blurring. The Alternating Direction Method of Multipliers (ADMM) method is used to optimally determine parameters, including τi, by solving an optimization problem. Each parameter is addressed separately, harnessing the benefits of ADMM. Our method introduces a novel parameter update approach and significantly improves retinal image quality, detecting cataracts, and diabetic retinopathy. Simulation results confirm our method’s superiority over existing state-of-the-art methods across various datasets.

show abstract

Retinal blood vessel segmentation from fundus image using an efficient multiscale directional representation technique Bendlets

Cited by 12 publications

References 1 publication

Retinal Vasculature Segmentation Based on Morphology and Pixel Level Classification

Retinal Vasculature Segmentation Based on Morphology and Pixel Level Classification

Blood Vessels Segmentation in Eye Fundus Images Using Image Processing Algorithms

Robust PCA with Lw,∗ and L2,1 Norms: A Novel Method for Low-Quality Retinal Image Enhancement

Contact Info

Product

Resources

About