Retinal image assessment using bi-level adaptive morphological component analysis

Javidi, Malihe; Harati, Ahad; Pourreza, Hamid Reza

doi:10.1016/j.artmed.2019.07.010

Cited by 13 publications

(5 citation statements)

References 67 publications

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“…Javidi et al 22 performed a novel bi‐level adaptive technique based on the morphological component analysis (BAMCA) which treated with the sparse representation using dictionary learning. The suitable dictionaries as K‐SVD learning scheme is highly appropriate for training the retinal images by extracting the feature maps.…”

Section: Related Workmentioning

confidence: 99%

UNIConv: An enhanced U‐Net based InceptionV3 convolutional model for DR semantic segmentation in retinal fundus images

Kaur

2022

Concurrency and Computation

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Segmentation of diabetic retinopathy is a significant requirement for correct severity estimation level. Due to dissimilarity in shapes, size, and quantity of retinal lesions, the procedure of labor-intensive type of grading becomes a more time-consuming and challenging task. It imposes the prerequisite for an automated segmentation framework that can accurately identify the regions with margins and aid ophthalmologists in the early detection and monitoring of the severity level. The proposed work introduces a customized U-Net model based on the InceptionV3 pre-trained CNN network. It uses a periodic shuffling approach along with sub-pixel convolution initialized to convolution nearest-neighbor resize. The model performed training and validation for non-proliferative diabetic retinopathy that is, microaneurysms, hemorrhages, hard and soft exudates on two standard datasets such as IDRiD and DIARETDB1. The proposed model achieves 99.82% accuracy, 88.39% sensitivity, and 99.94% specificity for hemorrhages, 99.95% accuracy, 70.76% sensitivity, and 99.98% specificity for microaneurysms, 99.83% accuracy, 85.9% sensitivity, and 99.95% specificity for hard exudates, along with 99.62% accuracy, 85.38% sensitivity, and 99.71% specificity for soft exudates segmentation on the IDRID dataset. In comparison with both datasets, the proposed system on the IDRID dataset performed accurately with superior state-of-the-art outcomes for segmentation of the retinal lesions.

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Section: Related Workmentioning

confidence: 99%

UNIConv: An enhanced U‐Net based InceptionV3 convolutional model for DR semantic segmentation in retinal fundus images

Kaur

2022

Concurrency and Computation

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“…Carrillo et al [12] analysed several generic features, such as the statistical features of histograms, cooccurrence matrices, run-length and cumulative probability of blur detection. Javidi et al [13] presented the morphological component analysis based framework. Beneficial from the adaptive representations via dictionary learning, it was adapted to work on retinal images with severe DR to simultaneously separate vessels and exudate lesions as diagnostically useful morphological components.…”

Section: Related Workmentioning

confidence: 99%

“…Javidi et al. [13] presented the morphological component analysis based framework. Beneficial from the adaptive representations via dictionary learning, it was adapted to work on retinal images with severe DR to simultaneously separate vessels and exudate lesions as diagnostically useful morphological components.…”

Section: Related Workmentioning

confidence: 99%

Blood vessel and background separation for retinal image quality assessment

Liu

et al. 2021

IET Image Processing

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Retinal image analysis has become an intuitive and standard aided diagnostic technique for eye diseases. The good image quality is essential support for doctors to provide timely and accurate disease diagnosis. This paper proposes an end‐to‐end learning based method for evaluating the retinal image quality. First, blood vessels of the input image are segmented by U‐Net, and the fundus image is divided into two parts: blood vessels and background. Then, we design a dual branch network module which extracts global features that influence the image quality and suppress the interference of blood vessels and local textures to achieve better performance. The proposed module can be embedded in various advanced network structures. The experimental results show the more efficient convergence rate for the network with the module. The best network accuracy rate is 85.83%, the AUC is 0.9296, and the F1‐score is 0.7967 on the collected local dataset. Additionally, the model generalization is tested on the public DRIMDB dataset. The accuracy, AUC, and F1‐score reach 97.89%, 0.9978, and 0.9688, respectively. Compared with the state‐of‐the‐art networks, the performance of the proposed method is proven to be accurate and effective for retinal image quality assessment.

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“…Besides, Quellec et al [ 24 ] proposed a deep learning algorithm supervised at image level and produced heatmaps to improve DR detection. Javidi et al presented dictionary learning-based algorithms to segment exudates using extension of morphological component analysis [ 25 ] and to detect microaneurysm using sparse representation [ 26 ]. Dai et al [ 27 ] combined an image-to-text model and multisieving CNN to identify microaneurysm and solve the unbalanced data distribution problem.…”

Section: Introductionmentioning

confidence: 99%

EAD-Net: A Novel Lesion Segmentation Method in Diabetic Retinopathy Using Neural Networks

Wan

Chen

et al. 2021

Disease Markers

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Diabetic retinopathy (DR) is a common chronic fundus disease, which has four different kinds of microvessel structure and microvascular lesions: microaneurysms (MAs), hemorrhages (HEs), hard exudates, and soft exudates. Accurate detection and counting of them are a basic but important work. The manual annotation of these lesions is a labor-intensive task in clinical analysis. To solve the problem, we proposed a novel segmentation method for different lesions in DR. Our method is based on a convolutional neural network and can be divided into encoder module, attention module, and decoder module, so we refer it as EAD-Net. After normalization and augmentation, the fundus images were sent to the EAD-Net for automated feature extraction and pixel-wise label prediction. Given the evaluation metrics based on the matching degree between detected candidates and ground truth lesions, our method achieved sensitivity of 92.77%, specificity of 99.98%, and accuracy of 99.97% on the e_ophtha_EX dataset and comparable AUPR (Area under Precision-Recall curve) scores on IDRiD dataset. Moreover, the results on the local dataset also show that our EAD-Net has better performance than original U-net in most metrics, especially in the sensitivity and F1-score, with nearly ten percent improvement. The proposed EAD-Net is a novel method based on clinical DR diagnosis. It has satisfactory results on the segmentation of four different kinds of lesions. These effective segmentations have important clinical significance in the monitoring and diagnosis of DR.

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Retinal image assessment using bi-level adaptive morphological component analysis

Cited by 13 publications

References 67 publications

UNIConv: An enhanced U‐Net based InceptionV3 convolutional model for DR semantic segmentation in retinal fundus images

UNIConv: An enhanced U‐Net based InceptionV3 convolutional model for DR semantic segmentation in retinal fundus images

Blood vessel and background separation for retinal image quality assessment

EAD-Net: A Novel Lesion Segmentation Method in Diabetic Retinopathy Using Neural Networks

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