Fovea Localization in Fundus Photographs by Faster R-CNN with Physiological Prior

Wu, Jun; Wang, Jiapei; Xu, Jie; Wang, Yiting; Wang, Kaiwei; Shang, Zongjiang; Ding, Dayong; Li, Xirong; Yang, Gang; Jin, Xuemin; Wang, Yanting; Dai, Fangfang; Fan, Jianping

doi:10.1007/978-3-030-32956-3_19

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…2) Evaluation in fovea localization: We conducted fovea localization of the enhanced fundus images to verify the localization performance gains brought about by the proposed method and the others. We utilized a pre-trained fovea localization framework based on Faster R-CNN [38], which had been trained on high-quality color fundus images with manual fovea localization, for fully automatic fovea localization on the low-quality images, with and without application of image enhancement approaches. To measure the precision of fovea localization, we used the Euclidean distance (denoted as d) between the predicted box center and the box center of the ground truth following [38] as the fovea localization error.…”

Section: Evaluation Over Color Fundus Imagesmentioning

confidence: 99%

Structure and Illumination Constrained GAN for Medical Image Enhancement

Liu

et al. 2021

IEEE Trans. Med. Imaging

111

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The development of medical imaging techniques has greatly supported clinical decision making. However, poor imaging quality, such as non-uniform illumination or imbalanced intensity, brings challenges for automated screening, analysis and diagnosis of diseases. Previously, bi-directional GANs (e.g., CycleGAN), have been proposed to improve the quality of input images without the requirement of paired images. However, these methods focus on global appearance, without imposing constraints on structure or illumination, which are essential features for medical image interpretation. In this paper, we propose a novel and versatile bi-directional GAN, named Structure and illumination constrained GAN (StillGAN), for medical image quality enhancement. Our StillGAN treats low-and high-quality images as two distinct domains, and introduces local structure and illumination constraints for learning both overall characteristics and local details. Extensive experiments on three medical image datasets (e.g., corneal confocal microscopy, retinal color fundus and endoscopy images) demonstrate that our method performs better than both conventional methods and other deep learning-based methods. In addition, we have investigated the impact of the proposed method on different medical image analysis and clinical tasks such as nerve segmentation, tortuosity grading, fovea localization and disease classification.

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Section: Evaluation Over Color Fundus Imagesmentioning

confidence: 99%

Structure and Illumination Constrained GAN for Medical Image Enhancement

Liu

et al. 2021

IEEE Trans. Med. Imaging

111

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“…For example, deep learning has been applied recently as a cutting-edge method for this detection. Furthermore, it was observed that Convolutional Neural Network (CNN)-based approaches have achieved good results in object detection, including fovea detection [6][7][8]. Bander et al [6] used a multistage deep learning approach to detect the optical disc and fovea in retinal images, while Wu et al [7] used faster R-CNN with physiological prior.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, it was observed that Convolutional Neural Network (CNN)-based approaches have achieved good results in object detection, including fovea detection [6][7][8]. Bander et al [6] used a multistage deep learning approach to detect the optical disc and fovea in retinal images, while Wu et al [7] used faster R-CNN with physiological prior. In addition, Hasan [8] utilized the DR-Net method, an end-to-end encoder-decoder network for fovea detection.…”

Section: Introductionmentioning

confidence: 99%

The Methods of Determining Temporal Direction Based on Asymmetric Information of the Optic Disc for Optimal Fovea Detection

Wibawa,

Sumiharto,

Harjoko

et al. 2023

Symmetry

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Accurate localization of the fovea in fundus images is essential for diagnosing retinal diseases. Existing methods often require extensive data and complex processes to achieve high accuracy, posing challenges for practical implementation. In this paper, we propose an effective and efficient approach for fovea detection using simple image processing operations and a geometric approach based on the optic disc’s position. A key contribution of this study is the successful determination of the temporal direction by leveraging readable asymmetries related to the optic disc and its surroundings. We discuss three methods based on asymmetry conditions, including blood vessel distribution, cup disc inclination, and optic disc location ratio, for detecting the temporal direction. This enables precise determination of the optimal foveal region of interest. Through this optimized fovea region, fovea detection is achieved using straightforward morphological and image processing operations. Extensive testing on popular datasets (DRIVE, DiaretDB1, and Messidor) demonstrates outstanding accuracy of 99.04% and a rapid execution time of 0.251 s per image. The utilization of asymmetrical conditions for temporal direction detection provides a significant advantage, offering high accuracy and efficiency while competing with existing methods.

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