The efficacy of microaneurysms detection with and without vessel segmentation in color retinal images

Tavakoli, Meysam; Nazar, Mahdieh; Mehdizadeh, Amir

doi:10.1117/12.2548527

Cited by 6 publications

(10 citation statements)

References 55 publications

(99 reference statements)

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“…Though these DL-based methods have achieved impressive performance, they are all under supervised framework requiring sufficient large scale accurately annotated images when training model. Besides automatic DR grading mentioned above, several DR detection/segmentation works [31]- [33] are proposed recently. Tavakoli et al [31] compared effects of two preprocessing methods, illumination equalization and top-hat transformation on retinal images to detect microaneurysms using combination of matching based approach and deep learning methods either in the normal fundus images or in the presence of DR; Tavakoli and Nazar [32] applied three retinal vessel segmentation methods including Laplacian-of-Gaussian, Canny edge detector, and Matched filter to compare results of microaneurysms detection using combination of unsupervised and supervised learning either in the normal images or in the presence of DR; Tavakoli et al [33] did microaneurysms detection step using combination of Laplacian-of-Gaussian and convolutional neural networks, and the experiments evaluate the accuracy of this work.…”

Section: A Diabetic Retinopathy Diagnosismentioning

confidence: 99%

Hybrid Graph Convolutional Network for Semi-Supervised Retinal Image Classification

Zhang¹,

Pan²,

Zhang

et al. 2021

IEEE Access

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Diabetic Retinopathy (DR) causes a significant health threat to the patient's vision with diabetic disease, which may result in blindness in severe situations. Various automatic DR diagnosis models have been proposed along with the development of deep learning, while there always relies on a large scale annotated data to train the network. However, annotating medical fundus images is cost-expensive and requires well-trained professional doctors to identity the DR grades. To overcome this drawback, this paper focuses on utilizing the easily-obtained unlabeled data with the help of limited annotated data to identify DR grades accurately. Hence we proposes a semi-supervised retinal image classification method by a Hybrid Graph Convolutional Network (HGCN). This HGCN network designs a modularity-based graph learning module and integrates Convolutional Neural Network (CNN) features into the graph representation by graph convolutional network. The synthesized hybrid features are optimized by a semi-supervised classification task which is assisted by a similarity-based pseudo label estimator. Through the proposed HGCN method, the retinal image classification model can be trained efficiently by partially labeled samples and the complicated annotating work is not required for the most retinal images. The experimental results on MESSIDOR dataset demonstrate the favorable performance of HGCN on semi-supervised retinal image classification, and the fully labeled data training also achieves an obvious superiority to the state-of-the-art supervised learning methods.

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Section: A Diabetic Retinopathy Diagnosismentioning

confidence: 99%

Hybrid Graph Convolutional Network for Semi-Supervised Retinal Image Classification

Zhang¹,

Pan²,

Zhang

et al. 2021

IEEE Access

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“…Highlighting certain vessels can also be a useful tool for bringing focus to them during medical presentations or discussions [ 27 ]. The automated or semi-automated edge-detection function will save time as compared to manual segmentation in vessel segmentation [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Interactive Blood Vessel Segmentation from Retinal Fundus Image Based on Canny Edge Detector

Ooi

Zunaina

Hamid

et al. 2021

Sensors

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Optometrists, ophthalmologists, orthoptists, and other trained medical professionals use fundus photography to monitor the progression of certain eye conditions or diseases. Segmentation of the vessel tree is an essential process of retinal analysis. In this paper, an interactive blood vessel segmentation from retinal fundus image based on Canny edge detection is proposed. Semi-automated segmentation of specific vessels can be done by simply moving the cursor across a particular vessel. The pre-processing stage includes the green color channel extraction, applying Contrast Limited Adaptive Histogram Equalization (CLAHE), and retinal outline removal. After that, the edge detection techniques, which are based on the Canny algorithm, will be applied. The vessels will be selected interactively on the developed graphical user interface (GUI). The program will draw out the vessel edges. After that, those vessel edges will be segmented to bring focus on its details or detect the abnormal vessel. This proposed approach is useful because different edge detection parameter settings can be applied to the same image to highlight particular vessels for analysis or presentation.

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“…This would allow more patients to be screened per year allowing the ophthalmologists to spend more time on those patients who can get the most benefit from their expertise. In the same vein, automated screening systems are able to remove a large number of the individuals who do not have DR, reducing the workload of the ophthalmologists [13], [14]. In this way using Computer-Aided Diagnosis (CAD) systems [15], [16] which apply automated computerized techniques [17], can lead to rapid and appropriate detection [18], [19].…”

Section: Introductionmentioning

confidence: 99%

Automated Microaneurysms Detection in Retinal Images Using Radon Transform and Supervised Learning: Application to Mass Screening of Diabetic Retinopathy

et al. 2021

Self Cite

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Detection of red lesions in color retinal images is a critical step to prevent the development of vision loss and blindness associated with diabetic retinopathy (DR). Microaneurysms (MAs) are the most frequently observed and are usually the first lesions to appear as a consequence of DR. Therefore, their detection is necessary for mass screening of DR. However, detecting these lesions is a challenging task because of the low image contrast, and the wide variation of imaging conditions. Recently, the emergence of computer-aided diagnosis systems offers promising approaches to detect these lesions for diagnostic purposes. In this paper we focus on developing unsupervised and supervised techniques to cope intelligently with the MAs detection problem. In the first step, the retinal images are preprocessed to remove background variation in order to achieve a high level of accuracy in the detection. In the main processing step, important landmarks such as the optic nerve head and retinal vessels are detected and masked using the Radon transform (RT) and multi-overlapping windows. Finally, the MAs are detected and numbered by using a combination of RT and a supervised support vector machine classifier. The method was tested on three publicly available datasets and a local database comprising a total of 749 images. Detection performance was evaluated using sensitivity, specificity, and FROC analysis. From the image analysis viewpoint, DR was detected with a sensitivity of 100% and a specificity of 93% on average across all of these databases. Moreover, from lesion-based analysis the proposed approach detected the MAs with sensitivity of 95.7% with an average of 7 false positives per image. These results compare favourably with the best of the published results to date.

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The efficacy of microaneurysms detection with and without vessel segmentation in color retinal images

Cited by 6 publications

References 55 publications

Hybrid Graph Convolutional Network for Semi-Supervised Retinal Image Classification

Hybrid Graph Convolutional Network for Semi-Supervised Retinal Image Classification

Interactive Blood Vessel Segmentation from Retinal Fundus Image Based on Canny Edge Detector

Automated Microaneurysms Detection in Retinal Images Using Radon Transform and Supervised Learning: Application to Mass Screening of Diabetic Retinopathy

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