Fast macula detection and application to retinal image quality assessment

Alais, Robin; Dokládal, Petr; Erginay, Ali; Figliuzzi, Bruno; Decencière, Étienne

doi:10.1016/j.bspc.2019.101567

Cited by 14 publications

(11 citation statements)

References 46 publications

(53 reference statements)

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“…Therefore, the general multicolour space fusion network was proposed to integrate the representation of RGB, HSV, and LAB colour spaces. Robin et al [20] proposed a lightweight RIQA algorithm for the macular region localization and evaluated the visibility of macular images through a lightweight CNN. If the macular region was clearly visible in the field of view, the image quality was qualified.…”

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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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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“…ey are quality assessment based on structural similarity entropy (EN), standard deviation (SD), multiscale structural similarity (MS-SSIM) [44], normalized feature mutual information (NFMI) [45], edge retention fusion quality indicator Q AB/F [46], and visual information fidelity degree VIFF [47]. ese metrics describe the fusion results from different perspectives, and the larger of those values indicate that more source image information is retained, and better fusion performance is achieved [48]. Figures 7-11 show the fusion results with different algorithms for the infrared and visible images of Figure 5.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Infrared and Visible Image Fusion with Hybrid Image Filtering

Zhang

Zhu

2020

Mathematical Problems in Engineering

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Image fusion is an important technique aiming to generate a composite image from multiple images of the same scene. Infrared and visible images can provide the same scene information from different aspects, which is useful for target recognition. But the existing fusion methods cannot well preserve the thermal radiation and appearance information simultaneously. Thus, we propose an infrared and visible image fusion method by hybrid image filtering. We represent the fusion problem with a divide and conquer strategy. A Gaussian filter is used to decompose the source images into base layers and detail layers. An improved co-occurrence filter fuses the detail layers for preserving the thermal radiation of the source images. A guided filter fuses the base layers for retaining the background appearance information of the source images. Superposition of the fused base layer and fused detail layer generates the final fusion image. Subjective visual and objective quantitative evaluations comparing with other fusion algorithms demonstrate the better performance of the proposed method.

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“…e green component of the RGB retinal image is used for preprocessing of improved blood vessel and optic disc segmentation in [22]. In [23], the green component of retinal image is also used to train a network to segment the macular region. In this paper, we extract the green component of color fundus photograph and enhance the details by unsharp masking (UM), a classical tool for sharpness enhancement [24] and has been applied to fundus photograph [25,26] and medical images [27,28], before calculating the entropy images.…”

Section: Introductionmentioning

confidence: 99%

Detection of Diabetic Retinopathy Using Bichannel Convolutional Neural Network

Pao

Lin

Chien

et al. 2020

Journal of Ophthalmology

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Deep learning of fundus photograph has emerged as a practical and cost-effective technique for automatic screening and diagnosis of severer diabetic retinopathy (DR). The entropy image of luminance of fundus photograph has been demonstrated to increase the detection performance for referable DR using a convolutional neural network- (CNN-) based system. In this paper, the entropy image computed by using the green component of fundus photograph is proposed. In addition, image enhancement by unsharp masking (UM) is utilized for preprocessing before calculating the entropy images. The bichannel CNN incorporating the features of both the entropy images of the gray level and the green component preprocessed by UM is also proposed to improve the detection performance of referable DR by deep learning.

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Fast macula detection and application to retinal image quality assessment

Cited by 14 publications

References 46 publications

Blood vessel and background separation for retinal image quality assessment

Blood vessel and background separation for retinal image quality assessment

Infrared and Visible Image Fusion with Hybrid Image Filtering

Detection of Diabetic Retinopathy Using Bichannel Convolutional Neural Network

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