Blood Vessel Segmentation of Retinal Image Based on Dense-U-Net Network

Li, Zhenwei; Jia, Mengli; Yang, Xiaoli; Xu, Min

doi:10.3390/mi12121478

Cited by 16 publications

(4 citation statements)

References 32 publications

(30 reference statements)

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“…Also, they used KNN classifier on Diabetic Retinopathy Database (DIARETDB1) dataset. In 2021, Li et al [29] introduces a network architecture combining U-Net with DenseNet model to improve micro vessel segmentation accuracy and completeness. Retinal vascular segmentation was done on the public DRIVE dataset.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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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“…Finally, due to the high-resolution spectral data, reflectance differences between channels are reduced, meaning that chosen ML algorithms must be able to handle high correlation to be effective. While addressing the challenge of thin object features, ML experts can also contribute to solving segmentation problems with similar characteristics in other domains such as medical imaging [32].…”

Section: Applicationsmentioning

confidence: 99%

HyperPRI: A Dataset of Hyperspectral Images for Underground Plant Root Study

Chang,

Chowdhry,

Song

et al. 2023

Preprint

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Collecting and analyzing hyperspectral imagery (HSI) of plant roots over time can enhance our understanding of their function, responses to environmental factors, turnover, and relationship with the rhizosphere. Current belowground red-green-blue (RGB) root imaging studies infer such functions from physical properties like root length, volume, and surface area. HSI provides a more complete spectral perspective of plants by capturing a high-resolution spectral signature of plant parts, which have extended studies beyond physical properties to include physiological properties, chemical composition, and phytopathology. Understanding crop plants’ physical, physiological, and chemical properties enables researchers to determine high-yielding, drought-resilient genotypes that can withstand climate changes and sustain future population needs. However, most HSI plant studies use cameras positioned above ground, and thus, similar belowground advances are urgently needed. One reason for the sparsity of belowground HSI studies is that root features often have limited distinguishing reflectance intensities compared to surrounding soil, potentially rendering conventional image analysis methods ineffective. In the field of machine learning (ML), there are currently no publicly available datasets containing the heavy correlation, highly textured background, and thin features characteristic of belowground root systems. Here we present HyperPRI, a novel dataset containing RGB and HSI data for in situ, non-destructive, underground plant root analysis using ML tools. HyperPRI contains images of plant roots grown in rhizoboxes for two annual crop species – peanut (Arachis hypogaea) and sweet corn (Zea mays). Drought conditions are simulated once, and the boxes are imaged and weighed on select days across two months. Along with the images, we provide hand-labeled semantic masks and imaging environment metadata. Additionally, we present baselines for root segmentation on this dataset and draw comparisons between methods that focus on spatial, spectral, and spatial-spectral features to predict the pixel-wise labels. Results demonstrate that combining HyperPRI’s hyperspectral and spatial information improves semantic segmentation of target objects.

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“…Retinal damage caused by various diseases can eventually lead to irreversible vision loss. With population aging becoming a major demographic trend worldwide, the number of patients with retinal diseases such as age-related macular degeneration (AMD) and diabetic retinopathy (DR) will increase year by year [ 1 , 2 , 3 ]. Other retinal diseases, including retinal vascular occlusion, hypertensive retinopathy, and retinitis, are important causes of visual impairment.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Label Detection Deep Learning Model with Attention-Guided Image Enhancement for Retinal Images

Yang

et al. 2023

Micromachines

Self Cite

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At present, multi-disease fundus image classification tasks still have the problems of small data volumes, uneven distributions, and low classification accuracy. In order to solve the problem of large data demand of deep learning models, a multi-disease fundus image classification ensemble model based on gradient-weighted class activation mapping (Grad-CAM) is proposed. The model uses VGG19 and ResNet50 as the classification networks. Grad-CAM is a data augmentation module used to obtain a network convolutional layer output activation map. Both the augmented and the original data are used as the input of the model to achieve the classification goal. The data augmentation module can guide the model to learn the feature differences of lesions in the fundus and enhance the robustness of the classification model. Model fine tuning and transfer learning are used to improve the accuracy of multiple classifiers. The proposed method is based on the RFMiD (Retinal Fundus Multi-Disease Image Dataset) dataset, and an ablation experiment was performed. Compared with other methods, the accuracy, precision, and recall of this model are 97%, 92%, and 81%, respectively. The resulting activation graph shows the areas of interest for model classification, making it easier to understand the classification network.

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Blood Vessel Segmentation of Retinal Image Based on Dense-U-Net Network

Cited by 16 publications

References 32 publications

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

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

HyperPRI: A Dataset of Hyperspectral Images for Underground Plant Root Study

A Multi-Label Detection Deep Learning Model with Attention-Guided Image Enhancement for Retinal Images

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