A review of retinal vessel segmentation for fundus image analysis

Qin, Qing; Chen, Yuanyuan

doi:10.1016/j.engappai.2023.107454

Cited by 18 publications

(4 citation statements)

References 59 publications

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“…Considering that the contrast between lesion areas and normal tissues in retinal fundus images is often imperceptible, it is difficult to distinguish between them with the naked eye and automated DR diagnosis based on deep learning [30]. Therefore, many methods [31,32] employ various techniques to highlight the lesion areas in the images before performing DR diagnosis, assisting the network to better process the images. Following the above-mentioned methods, this paper applies the following preprocessing steps to the DDR dataset, DIARETDB1 dataset, and IDRiD dataset: First, the black borders are cropped to reduce computation time and suppress irrelevant noise information.…”

Section: Data Preprocessmentioning

confidence: 99%

Diabetic Retinopathy Lesion Segmentation Method Based on Multi-Scale Attention and Lesion Perception

Bian,

Si,

Wang

2024

Algorithms

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The early diagnosis of diabetic retinopathy (DR) can effectively prevent irreversible vision loss and assist ophthalmologists in providing timely and accurate treatment plans. However, the existing methods based on deep learning have a weak perception ability of different scale information in retinal fundus images, and the segmentation capability of subtle lesions is also insufficient. This paper aims to address these issues and proposes MLNet for DR lesion segmentation, which mainly consists of the Multi-Scale Attention Block (MSAB) and the Lesion Perception Block (LPB). The MSAB is designed to capture multi-scale lesion features in fundus images, while the LPB perceives subtle lesions in depth. In addition, a novel loss function with tailored lesion weight is designed to reduce the influence of imbalanced datasets on the algorithm. The performance comparison between MLNet and other state-of-the-art methods is carried out in the DDR dataset and DIARETDB1 dataset, and MLNet achieves the best results of 51.81% mAUPR, 49.85% mDice, and 37.19% mIoU in the DDR dataset, and 67.16% mAUPR and 61.82% mDice in the DIARETDB1 dataset. The generalization experiment of MLNet in the IDRiD dataset achieves 59.54% mAUPR, which is the best among other methods. The results show that MLNet has outstanding DR lesion segmentation ability.

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Section: Data Preprocessmentioning

confidence: 99%

Diabetic Retinopathy Lesion Segmentation Method Based on Multi-Scale Attention and Lesion Perception

Bian,

Si,

Wang

2024

Algorithms

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“…Although this methodology is versatile and applicable in various domains involving computer vision, it is important to note that the nature of medical images can be highly diverse, presenting unique segmentation difficulties. Moreover, medical imaging datasets are often limited in their size and scope [ [15] , [16] , [17] ]. However, despite the interesting results of these approaches, they have critical drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Image segmentation with Cellular Automata

Ascencio-Piña,

García-De-Lira,

Cuevas

et al. 2024

Heliyon

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“…Artificial intelligence employs image processing and deep learning models to accurately detect and classify cases of DR [3]. The integration of artificial intelligence techniques into retinal vascular segmentation has emerged because of the progressive advancement and expansion of this field [4]. However, traditional 2 methods for diagnosing DR, primarily involving manual segmentation by ophthalmologists, are labor-intensive, time-consuming, and subject to variability in results [5].…”

Section: Introductionmentioning

confidence: 99%

Advanced Detection of Diabetic Retinopathy: Employing Hybrid Deep Neural Networks and Multi-Scale Image Analysis Techniques

Mutawa,

Hemalakshmi,

Prakash

et al. 2024

Preprint

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Diabetic retinopathy (DR), a major complication of prolonged diabetes, poses a significant risk of vision loss. Early detection is critical for effective treatment, yet traditional diagnostic methods by ophthalmologists are time-consuming, costly, and subject to variability. This study introduces a novel approach employing a hybrid Convolutional Neural Network-Radial Basis Function (CNN-RBF) classifier integrated with Multi-Scale Discriminative Robust Local Binary Pattern (MS-DRLBP) features for enhanced DR detection. We implemented advanced image preprocessing techniques, including noise reduction, morphological operations, and Otsu’s thresholding, to optimize blood vessel segmentation from retinal images. Our method demonstrates exceptional performance in screening DR, achieving an average of 96.10% precision, 95.35% sensitivity, 97.06% specificity, and 96.10% accuracy. These results significantly outperform traditional methods and offer a promising tool for remote and efficient screening of DR. Applied to publicly available datasets, this research contributes to the development of accessible, accurate diagnostic methods in ophthalmology, potentially reducing the global burden of diabetic vision loss.

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A review of retinal vessel segmentation for fundus image analysis

Cited by 18 publications

References 59 publications

Diabetic Retinopathy Lesion Segmentation Method Based on Multi-Scale Attention and Lesion Perception

Diabetic Retinopathy Lesion Segmentation Method Based on Multi-Scale Attention and Lesion Perception

Image segmentation with Cellular Automata

Advanced Detection of Diabetic Retinopathy: Employing Hybrid Deep Neural Networks and Multi-Scale Image Analysis Techniques

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