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Journal articles on the topic 'Fundus Image'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Starovoitov, V. V., Y. I. Golub, and M. M. Lukashevich. "Digital fundus image quality assessment." «System analysis and applied information science», no. 4 (January 5, 2022): 25–38. http://dx.doi.org/10.21122/2309-4923-2021-4-25-38.

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Diabetic retinopathy (DR) is a disease caused by complications of diabetes. It starts asymptomatically and can end in blindness. To detect it, doctors use special fundus cameras that allow them to register images of the retina in the visible range of the spectrum. On these images one can see features, which determine the presence of DR and its grade. Researchers around the world are developing systems for the automated analysis of fundus images. At present, the level of accuracy of classification of diseases caused by DR by systems based on machine learning is comparable to the level of qualif

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Wintergerst, Maximilian W. M., Linus G. Jansen, Frank G. Holz, and Robert P. Finger. "A Novel Device for Smartphone-Based Fundus Imaging and Documentation in Clinical Practice: Comparative Image Analysis Study." JMIR mHealth and uHealth 8, no. 7 (2020): e17480. http://dx.doi.org/10.2196/17480.

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Background Smartphone-based fundus imaging allows for mobile and inexpensive fundus examination with the potential to revolutionize eye care, particularly in lower-resource settings. However, most smartphone-based fundus imaging adapters convey image quality not comparable to conventional fundus imaging. Objective The purpose of this study was to evaluate a novel smartphone-based fundus imaging device for documentation of a variety of retinal/vitreous pathologies in a patient sample with wide refraction and age ranges. Methods Participants’ eyes were dilated and imaged with the iC2 funduscope

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Wang, Shuang. "Research on Fundus Image Mosaic Method Based on Genetic Algorithm." Advances in Multimedia 2021 (November 30, 2021): 1–7. http://dx.doi.org/10.1155/2021/6060691.

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Retinal image mosaic is the key to detect common diseases, and the existing image mosaic methods are difficult to solve the problems of low contrast of fundus images and geometric distortion between images in different fields of view. To solve the problem of noise in retinal fundus images, an image mosaic algorithm based on the genetic algorithm was proposed. Firstly, a series of morphological pretreatment was performed on the fundus images. Then, the vascular network is extracted by obtaining the maximum entropy of the image to determine the threshold value. The similarity of the image to be

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Chen, Shizhao, Qian Zhou, and Hua Zou. "A Novel Un-Supervised GAN for Fundus Image Enhancement with Classification Prior Loss." Electronics 11, no. 7 (2022): 1000. http://dx.doi.org/10.3390/electronics11071000.

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Fundus images captured for clinical diagnosis usually suffer from degradation factors due to variation in equipment, operators, or environment. These degraded fundus images need to be enhanced to achieve better diagnosis and improve the results of downstream tasks. As there is no paired low- and high-quality fundus image, existing methods mainly focus on supervised or semi-supervised learning methods for color fundus image enhancement (CFIE) tasks by utilizing synthetic image pairs. Consequently, domain gaps between real images and synthetic images arise. With respect to existing unsupervised

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Li, Zhenwei, Mengying Xu, Xiaoli Yang, and Yanqi Han. "Multi-Label Fundus Image Classification Using Attention Mechanisms and Feature Fusion." Micromachines 13, no. 6 (2022): 947. http://dx.doi.org/10.3390/mi13060947.

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Fundus diseases can cause irreversible vision loss in both eyes if not diagnosed and treated immediately. Due to the complexity of fundus diseases, the probability of fundus images containing two or more diseases is extremely high, while existing deep learning-based fundus image classification algorithms have low diagnostic accuracy in multi-labeled fundus images. In this paper, a multi-label classification of fundus disease with binocular fundus images is presented, using a neural network algorithm model based on attention mechanisms and feature fusion. The algorithm highlights detailed featu

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Dai, Peishan, Hanwei Sheng, Jianmei Zhang, Ling Li, Jing Wu, and Min Fan. "Retinal Fundus Image Enhancement Using the Normalized Convolution and Noise Removing." International Journal of Biomedical Imaging 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/5075612.

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Retinal fundus image plays an important role in the diagnosis of retinal related diseases. The detailed information of the retinal fundus image such as small vessels, microaneurysms, and exudates may be in low contrast, and retinal image enhancement usually gives help to analyze diseases related to retinal fundus image. Current image enhancement methods may lead to artificial boundaries, abrupt changes in color levels, and the loss of image detail. In order to avoid these side effects, a new retinal fundus image enhancement method is proposed. First, the original retinal fundus image was proce

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Gervelmeyer, Julius, Sarah Müller, Ziwei Huang, and Philipp Berens. "Fundus Image Toolbox: A Python package for fundus image processing." Journal of Open Source Software 10, no. 108 (2025): 7101. https://doi.org/10.21105/joss.07101.

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Zhao, Wen Dong, You Dong Zhang, and Chun Xia Jin. "A New Method of Fundus Image Enhancement Based on Rough Set and Wavelet Transform." Applied Mechanics and Materials 397-400 (September 2013): 2205–8. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.2205.

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Fundus images are complex images with more details, and on the basis of the inadequate fuzzy enhancement algorithm proposed by Pal et al, this article propose an improved algorithm of rough set for fundus image enhancement. The fundus image will be multi-scale decomposed by wavelet transform firstly, and then the subgraphs are enhanced by using rough set to improve the visual effects; finally the processed sub-images will be reconstructed and generated a new-enhanced image. Compared with the Pal algorithm, the new algorithm not only overcomes its weaknesses that the threshold is set with a fix

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Kapoor, Preeti, and Shaveta Arora. "Fundus Image Generation using EyeGAN." Journal of Computers, Mechanical and Management 2, no. 6 (2023): 9–17. http://dx.doi.org/10.57159/gadl.jcmm.2.6.230106.

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Deep learning models are widely used in various computer vision fields ranging from classification, segmentation to identification, but these models suffer from the problem of overfitting. Diversifying and balancing the datasets is a solution to the primary problem. Generative Adversarial Networks (GANs) are unsupervised learning image generators which do not require any additional information. GANs generate realistic images and preserve the minute details from the original data. In this paper, a GAN model is proposed for fundus image generation to overcome the problem of labelled data insuffi

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Krzywicki, Tomasz, Piotr Brona, Agnieszka M. Zbrzezny, and Andrzej E. Grzybowski. "A Global Review of Publicly Available Datasets Containing Fundus Images: Characteristics, Barriers to Access, Usability, and Generalizability." Journal of Clinical Medicine 12, no. 10 (2023): 3587. http://dx.doi.org/10.3390/jcm12103587.

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This article provides a comprehensive and up-to-date overview of the repositories that contain color fundus images. We analyzed them regarding availability and legality, presented the datasets’ characteristics, and identified labeled and unlabeled image sets. This study aimed to complete all publicly available color fundus image datasets to create a central catalog of available color fundus image datasets.

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Ahn, Sangil, Quang T. M. Pham, Jitae Shin, and Su Jeong Song. "Future Image Synthesis for Diabetic Retinopathy Based on the Lesion Occurrence Probability." Electronics 10, no. 6 (2021): 726. http://dx.doi.org/10.3390/electronics10060726.

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Diabetic Retinopathy (DR) is one of the major causes of blindness. If the lesions observed in DR occur in the central part of the fundus, it can cause severe vision loss, and we call this symptom Diabetic Macular Edema (DME). All patients with DR potentially have DME since DME can occur in every stage of DR. While synthesizing future fundus images, the task of predicting the progression of the disease state is very challenging since we need a lot of longitudinal data over a long period of time. Even if the longitudinal data are collected, there is a pixel-level difference between the current f

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Kammala, Sai Sravanthi. "Identifying glaucoma fundus images using image processing." International Journal of Communication and Information Technology 1, no. 2 (2020): 33–36. http://dx.doi.org/10.33545/2707661x.2020.v1.i2a.17.

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T. Nandhini. "Rectal Fundus Image Recognition Using Deep Learning Ensemble CNN Models." Journal of Information Systems Engineering and Management 10, no. 26s (2025): 690–702. https://doi.org/10.52783/jisem.v10i26s.4276.

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As a vital tool for ophthalmologists to detect potential blinding problems, retinal fundus image analysis is an essential part of medical image analysis. This work looks at retinal fundus images for the purpose of detecting diabetic retinopathy. After every model has been trained independently, the probabilities for every class are totalled to get the ultimate value. The class with the greatest value is called the decision class. Because of this, the proposed methodology consists of the following steps: gathering retinal fundus images; pre-processing images (resizing, contrast enhancement, sha

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Hernandez-Matas, Carlos, Xenophon Zabulis, Areti Triantafyllou, Panagiota Anyfanti, Stella Douma, and Antonis A. Argyros. "FIRE: Fundus Image Registration dataset." Modeling and Artificial Intelligence in Ophthalmology 1, no. 4 (2017): 16–28. http://dx.doi.org/10.35119/maio.v1i4.42.

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Purpose: Retinal image registration is a useful tool for medical professionals. However, performance evaluation of registration methods has not been consistently assessed in the literature. To address that, a dataset comprised of retinal image pairs annotated with ground truth and an evaluation protocol for registration methods is proposed.Methods: The dataset is comprised by 134 retinal fundus image pairs. These pairs are classified into three categories, according to characteristics that are relevant to indicative registration applications. Such characteristics are the degree of overlap betw

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Toresa, Dafwen, Fana Wiza, Keumala Anggraini, Taslim Taslim, Edriyansyah, and Lisnawita Lisnawita. "Comparison of Image Enhancement Methods for Diabetic Retinopathy Screening." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, no. 5 (2023): 1111–17. http://dx.doi.org/10.29207/resti.v7i5.5193.

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The most common factor contributing to visual abnormalities that result in blindness is known as diabetic retinopathy (DR). Retinal fundus scanning, a non-invasive method that is integral to the picture pre-processing phase, can be used to identify and monitor DR. Low intensity, irregular lighting, and inhomogeneous color are some of the main issues with DR fundus photographs. Analysis of aberrant characteristics on retinal fundus pictures to identify diabetic retinopathy is one of the key responsibilities of image enhancement. However, a variety of approaches have been created, and it is unkn

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Shen, Jing-Jing, Li-Long Wang, Chuan-Feng Lyu, et al. "Image enhancement of color fundus photographs for age-related macular degeneration: the Shanghai Changfeng Study." International Journal of Ophthalmology 15, no. 2 (2022): 268–75. http://dx.doi.org/10.18240/ijo.2022.02.12.

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AIM: To develop and evaluate a new fundus image optimization software based on red, green, blue channels (RGB) for the evaluation of age-related macular degeneration (AMD) in the Chinese population. METHODS: Fundus images that were diagnosed as AMD from the Shanghai Changfeng Study database were analyzed to develop a standardized optimization procedure. Image brightness, contrast, and color balance were measured. Differences between central lesion area and normal retinal area under different image brightness, contrast, and color balance were observed. The optimal optimization parameters were d

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Wanling, Wu, and Noraisyah Mohamed Shah. "Fundus Image Enhancement using CLAHE." Journal of New Explorations in Electrical Engineering 1, no. 1 (2025): 67–78. https://doi.org/10.22452/nece.vol1no1.6.

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Fundus retinal images are crucial for ophthalmologists to diagnose diseases and monitor changes in the condition. However, due to factors such as lighting conditions, instrument effects, and individual differences, fundus images often have the drawbacks of low contrast and lack of details. To improve the quality and accuracy of images, contrast enhancement technology for fundus images has become a research hotspot. This paper proposes a new CLAHE (Contrast Limited Adaptive Histogram Equalization) method to improve the brightness and contrast of retinal images. The method improves the luminosit

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Malerbi, Fernando Korn. "Fundus image for neurologists." Headache Medicine 13, no. 3 (2022): 160–62. http://dx.doi.org/10.48208/headachemed.2022.14.

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Chen, Yiwei, Yi He, Wanyue Li, et al. "Series-Parallel Generative Adversarial Network Architecture for Translating from Fundus Structure Image to Fluorescence Angiography." Applied Sciences 12, no. 20 (2022): 10673. http://dx.doi.org/10.3390/app122010673.

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Although fundus fluorescein angiography (FFA) is a very effective retinal imaging tool for ophthalmic diagnosis, the requirement of intravenous injection of harmful fluorescein dye limits its application. As a screening diagnostic method that reduces the frequency of intravenous injection, a series-parallel generative adversarial network (GAN) architecture for translating fundus structure image to FFA images is proposed herein, using deep learning-based software that only needs an intravenous injection for the training process. Firstly, the fundus structure image and the corresponding FFA imag

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B, Aavani. "Automated Detection and Classification of Diabetic Retinopathy using ConvNets." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (2021): 1360–69. http://dx.doi.org/10.22214/ijraset.2021.38628.

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Abstract: Diabetic retinopathy is the leading cause of blindness in diabetic patients. Screening of diabetic retinopathy using fundus image is the most effective way. As the time increases this DR leads to permanent loss of vision. At present, Diabetic retinopathy is still being treated by hand by an ophthalmologist which is a time-consuming process. Computer aided and fully automatic diagnosis of DR plays an important role in now a day. Data-set containing a collection of fundus images of different severity scale is used to analyze the fundus image of DR patients. Here the deep neural network

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Chan, Tsung-Yueh, Jen-Hung Wang, Nancy Chen, and Cheng-Jen Chiu. "The Assessment of Retinal Image Quality Using a Non-Mydriatic Fundus Camera in a Teleophthalmologic Platform." Diagnostics 14, no. 14 (2024): 1543. http://dx.doi.org/10.3390/diagnostics14141543.

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This study assesses the quality of retinal images captured using a non-mydriatic fundus camera within a teleophthalmologic platform in Taiwan. The objective was to evaluate the effectiveness of non-mydriatic fundus cameras for remote retinal screening and identify factors impacting image quality. From June 2020 to August 2022, 629 patients from five rural infirmaries underwent ophthalmic examinations, with fundus images captured without pupil dilation. These images were reviewed by senior ophthalmologists and graded based on quality. The results indicated that approximately 70% of images were

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Long, Tengfei, Yi Xu, Haidong Zou, et al. "A Generic Pixel Pitch Calibration Method for Fundus Camera via Automated ROI Extraction." Sensors 22, no. 21 (2022): 8565. http://dx.doi.org/10.3390/s22218565.

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Pixel pitch calibration is an essential step to make the fundus structures in the fundus image quantitatively measurable, which is important for the diagnosis and treatment of many diseases, e.g., diabetes, arteriosclerosis, hereditary optic atrophy, etc. The conventional calibration approaches require the specific parameters of the fundus camera or several specially shot images of the chess board, but these are generally not accessible, and the calibration results cannot be generalized to other cameras. Based on automated ROI (region of interest) and optic disc detection, the diameter ratio o

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Sharma, Himanshu, Javed Wasim, and Pankaj Sharma. "An Efficient System for Identification of Eye Disease in Fundus Images using a Deep Transfer Learning-based Pre-trained Model." Engineering, Technology & Applied Science Research 14, no. 5 (2024): 17398–404. http://dx.doi.org/10.48084/etasr.8408.

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Ophthalmologists rely heavily on retinal fundus imaging to diagnose retinal diseases. Early detection can enhance the likelihood of a cure and also prevent blindness. Retinal fundus images can be used by medical professionals to diagnose retinal conditions such as diabetic retinopathy and retinitis pigmentosa. This study proposes an automated diagnostic approach using a Deep Learning (DL) model to identify fundus images with a high prediction rate. This study aims to use multilabel classification to identify diseases in fundus images. An EfficientNet-B5-based model was trained on a fundus imag

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Fauzi, Ahmad, and Lukmanda Evan Lubis. "Optimization of retinal blood vessel segmentation based on Gabor filters and particle swarm optimization." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 3 (2023): 1590. http://dx.doi.org/10.11591/ijeecs.v29.i3.pp1590-1596.

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The structure of the retinal blood vessels can be obtained by segmenting the fundus images. A fundus image can be gained through color fundus photography or fluorescein angiography (FA). The fundus image produced by the camera can cause noise which can reduce the quality of the fundus image. To reduce the noise, this research uses the non-local means filter (NLMF). For texture analysis, the study uses Gabor filters due to the frequencies of this filter as the same as the human visual system. The segmenting process of the retinal blood vessel is performed using K-means optimized by particle swa

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Ahmad, Fauzi, and Evan Lubis Lukmanda. "Optimization of retinal blood vessel segmentation based on Gabor filters and particle swarm optimization." Optimization of retinal blood vessel segmentation based on Gabor filters and particle swarm optimization 29, no. 3 (2023): 1590–96. https://doi.org/10.11591/ijeecs.v29.i3.pp1590-1596.

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The structure of the retinal blood vessels can be obtained by segmenting the fundus images. A fundus image can be gained through color fundus photography or fluorescein angiography (FA). The fundus image produced by the camera can cause noise which can reduce the quality of the fundus image. To reduce the noise, this research uses the non-local means filter (NLMF). For texture analysis, the study uses Gabor filters due to the frequencies of this filter as the same as the human visual system. The segmenting process of the retinal blood vessel is performed using K-means optimized by particle swa

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Lee, Kang Geon, Su Jeong Song, Soochahn Lee, Hyeong Gon Yu, Dong Ik Kim, and Kyoung Mu Lee. "A deep learning-based framework for retinal fundus image enhancement." PLOS ONE 18, no. 3 (2023): e0282416. http://dx.doi.org/10.1371/journal.pone.0282416.

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Problem Low-quality fundus images with complex degredation can cause costly re-examinations of patients or inaccurate clinical diagnosis. Aim This study aims to create an automatic fundus macular image enhancement framework to improve low-quality fundus images and remove complex image degradation. Method We propose a new deep learning-based model that automatically enhances low-quality retinal fundus images that suffer from complex degradation. We collected a dataset, comprising 1068 pairs of high-quality (HQ) and low-quality (LQ) fundus images from the Kangbuk Samsung Hospital’s health screen

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Kaur, Kiranjit, and Priyadarshni. "Retinal Fundus Detection Using Skew Symmetric Matrix." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 7 (2017): 103. http://dx.doi.org/10.23956/ijarcsse.v7i7.107.

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The retina is the light sensitive tissue, lining the back of our eye. Light rays are focused onto the retina through our cornea, pupil and lens. The retina converts the light rays into impulses that travel through optic nerve to our brain, where they are interpreted as the images. The task of manually segmenting fundus from retina images is generally time-consuming and difficult. In most settings, the task is done by marking the fundus regions slice-by-slice, which limits the human rater’s view and generates distorted images. Manual segmentation is also typically done largely based on a single

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Toresa, Dafwen, Fana Wiza, Ahmad Ade Irwanda, Wenti Sasparita Abiyus, Edriyansyah Edriyansyah, and Taslim Taslim. "The Cuckoo Optimization Algorithm Enhanced Visualization of Morphological Features of Diabetic Retinopathy." Journal of Applied Engineering and Technological Science (JAETS) 4, no. 2 (2023): 929–39. http://dx.doi.org/10.37385/jaets.v4i2.1978.

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This research compares strategies for identifying diabetic retinopathy (DR) using fundus image and discusses the efficiency of various image pre-processing techniques to enhance the quality of fundus images. Fundus images in medical image processing often suffer from non-uniform lighting, low contrast, and noise issues, which necessitate image pre-processing to enhance their quality. The study evaluates the effectiveness of several optimization techniques in selecting the best technique for identifying DR. One of the image pre-processing techniques compared in the study involves comparing nega

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(Corresponding Author), Senthil kumar Arunachalam, Somasundaram Devaraj, and Bhavani Sridharan. "DEEP PERONA–MALIK DIFFUSIVE MEAN SHIFT IMAGE CLASSIFICATION FOR EARLY GLAUCOMA AND STARGARDT DISEASE DETECTION." Malaysian Journal of Computer Science 36, no. 1 (2023): 14–39. http://dx.doi.org/10.22452/mjcs.vol36no1.2.

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Glaucoma and Stargardt’s, an inherited disease predominantly affect the retinal portion of the eye. The diagnosis of Glaucoma in a fundus image is an arduous, time consuming process. There were many research works carried out to detect early stages of Glaucoma and Stargardt’s disease. However, the accuracy, diagnostic time and performance were not improved. To resolve the above said problems, a computational method called Deep Neural Perona–Malik Diffusive Mean Shift Mode Seeking Segmented Image Classification (DNP-MDMSMSIC) is introduced for the early detection of Glaucoma and Stargardt’s dis

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Tummala, Sudhakar, Venkata Sainath Gupta Thadikemalla, Seifedine Kadry, Mohamed Sharaf, and Hafiz Tayyab Rauf. "EfficientNetV2 Based Ensemble Model for Quality Estimation of Diabetic Retinopathy Images from DeepDRiD." Diagnostics 13, no. 4 (2023): 622. http://dx.doi.org/10.3390/diagnostics13040622.

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Diabetic retinopathy (DR) is one of the major complications caused by diabetes and is usually identified from retinal fundus images. Screening of DR from digital fundus images could be time-consuming and error-prone for ophthalmologists. For efficient DR screening, good quality of the fundus image is essential and thereby reduces diagnostic errors. Hence, in this work, an automated method for quality estimation (QE) of digital fundus images using an ensemble of recent state-of-the-art EfficientNetV2 deep neural network models is proposed. The ensemble method was cross-validated and tested on o

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Prastyo, Pulung Hendro, Amin Siddiq Sumi, and Annis Nuraini. "Optic Cup Segmentation using U-Net Architecture on Retinal Fundus Image." JITCE (Journal of Information Technology and Computer Engineering) 4, no. 02 (2020): 105–9. http://dx.doi.org/10.25077/jitce.4.02.105-109.2020.

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Retinal fundus images are used by ophthalmologists to diagnose eye disease, such as glaucoma disease. The diagnosis of glaucoma is done by measuring changes in the cup-to-disc ratio. Segmenting the optic cup helps petrify ophthalmologists calculate the CDR of the retinal fundus image. This study proposed a deep learning approach using U-Net architecture to carry out segmentation task. This proposed method was evaluated on 650 color retinal fundus image. Then, U-Net was configured using 160 epochs, image input size = 128x128, Batch size = 32, optimizer = Adam, and loss function = Binary Cross E

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Ochoa-Astorga, Jesús Eduardo, Linni Wang, Weiwei Du, and Yahui Peng. "Enhanced Vascular Bifurcations Mapping: Refining Fundus Image Registration." Electronics 13, no. 9 (2024): 1736. http://dx.doi.org/10.3390/electronics13091736.

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Fundus image registration plays a crucial role in the clinical evaluation of ocular diseases, such as diabetic retinopathy and macular degeneration, necessitating meticulous monitoring. The alignment of multiple fundus images enables the longitudinal analysis of patient progression, widening the visual scope, or augmenting resolution for detailed examinations. Currently, prevalent methodologies rely on feature-based approaches for fundus registration. However, certain methods exhibit high feature point density, posing challenges in matching due to point similarity. This study introduces a nove

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Firdaus Ahmad Fadzil, Ahmad, Zaaba Ahmad, Noor Elaiza Abd Khalid, and Shafaf Ibrahim. "Retinal Fundus Image Blood Vessels Segmentation via Object-Oriented Metadata Structures." International Journal of Engineering & Technology 7, no. 4.33 (2018): 110. http://dx.doi.org/10.14419/ijet.v7i4.33.23511.

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Retinal fundus image is a crucial tool for ophthalmologists to diagnose eye-related diseases. These images provide visual information of the interior layer of the retina structures such as optic disc, optic cup, blood vessels and macula that can assist ophthalmologist in determining the health of an eye. Segmentation of blood vessels in fundus images is one of the most fundamental phase in detecting diseases such as diabetic retinopathy. However, the ambiguity of the retina structures in the retinal fundus images presents a challenge for researcher to segment the blood vessels. Extensive pre-p

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Ramasubramanian, B., and S. Selvaperumal. "A Novel Efficient Approach for the Screening of New Abnormal Blood Vessels in Color Fundus Images." Applied Mechanics and Materials 573 (June 2014): 808–13. http://dx.doi.org/10.4028/www.scientific.net/amm.573.808.

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Reliable detection of abnormal vessels in color fundus image is still a great issue in medical image processing. An Efficient and robust approach for automatic detection of abnormal blood vessels in digital color fundus images is presented in this paper. First, the fundus images are preprocessed by applying a 3x3 median filter. Then, the images are segmented using a novel morphological operation. To classify these segmented image into normal and abnormal, seven features based on shape, contrast, position and density are extracted. Finally, these features are classified using a non-linear Suppo

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Hu, Yanzhe, Yu Li, Hua Zou, and Xuedong Zhang. "An Unsupervised Fundus Image Enhancement Method with Multi-Scale Transformer and Unreferenced Loss." Electronics 12, no. 13 (2023): 2941. http://dx.doi.org/10.3390/electronics12132941.

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Color fundus images are now widely used in computer-aided analysis systems for ophthalmic diseases. However, fundus imaging can be affected by human, environmental, and equipment factors, which may result in low-quality images. Such quality fundus images will interfere with computer-aided diagnosis. Existing methods for enhancing low-quality fundus images focus more on the overall visualization of the image rather than capturing pathological and structural features at the finer scales of the fundus image sufficiently. In this paper, we design an unsupervised method that integrates a multi-scal

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Pao, Shu-I., Hong-Zin Lin, Ke-Hung Chien, Ming-Cheng Tai, Jiann-Torng Chen, and Gen-Min Lin. "Detection of Diabetic Retinopathy Using Bichannel Convolutional Neural Network." Journal of Ophthalmology 2020 (June 20, 2020): 1–7. http://dx.doi.org/10.1155/2020/9139713.

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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 CN

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Ali, Aziah, Aini Hussain, Wan Mimi Diyana Wan Zaki, Wan Haslina Wan Abdul Halim, Wan Noorshahida Mohd Isa, and Noramiza Hashim. "Improved retinal vessel segmentation using the enhanced pre-processing method for high resolution fundus images." F1000Research 10 (December 1, 2021): 1222. http://dx.doi.org/10.12688/f1000research.73397.1.

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Background: By diagnosing using fundus images, ophthalmologists can possibly detect symptoms of retinal diseases such as diabetic retinopathy, age-related macular degeneration, and retinal detachment. A number of studies have also found some links between fundus image analysis data and other underlying systemic diseases such as cardiovascular diseases, including hypertension and kidney dysfunction. Now that imaging technology is advancing further, more fundus cameras are currently equipped with the capability to produce high resolution fundus images. One of the public databases for high-resolu

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Sundaram, Ramakrishnan, Ravichandran KS, Premaladha Jayaraman, and Venkatraman B. "Extraction of Blood Vessels in Fundus Images of Retina through Hybrid Segmentation Approach." Mathematics 7, no. 2 (2019): 169. http://dx.doi.org/10.3390/math7020169.

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A hybrid segmentation algorithm is proposed is this paper to extract the blood vesselsfrom the fundus image of retina. Fundus camera captures the posterior surface of the eye and thecaptured images are used to diagnose diseases, like Diabetic Retinopathy, Retinoblastoma, Retinalhaemorrhage, etc. Segmentation or extraction of blood vessels is highly required, since the analysisof vessels is crucial for diagnosis, treatment planning, and execution of clinical outcomes in the fieldof ophthalmology. It is derived from the literature review that no unique segmentation algorithm issuitable for image

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Lavanya, R., G. K. Rajini, and G. Vidhya Sagar. "Retinal vessel feature extraction from fundus image using image processing techniques." International Journal of Engineering & Technology 7, no. 2 (2018): 687. http://dx.doi.org/10.14419/ijet.v7i2.8892.

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Retinal Vessel detection for retinal images play crucial role in medical field for proper diagnosis and treatment of various diseases like diabetic retinopathy, hypertensive retinopathy etc. This paper deals with image processing techniques for automatic analysis of blood vessel detection of fundus retinal image using MATLAB tool. This approach uses intensity information and local phase based enhancement filter techniques and morphological operators to provide better accuracy.Objective: The effect of diabetes on the eye is called Diabetic Retinopathy. At the early stages of the disease, blood

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Qureshi, Imran, Jun Ma, and Kashif Shaheed. "A Hybrid Proposed Fundus Image Enhancement Framework for Diabetic Retinopathy." Algorithms 12, no. 1 (2019): 14. http://dx.doi.org/10.3390/a12010014.

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Diabetic retinopathy (DR) is a complication of diabetes and is known as visual impairment, and is diagnosed in various ethnicities of the working-age population worldwide. Fundus angiography is a widely applicable modality used by ophthalmologists and computerized applications to detect DR-based clinical features such as microaneurysms (MAs), hemorrhages (HEMs), and exudates (EXs) for early screening of DR. Fundus images are usually acquired using funduscopic cameras in varied light conditions and angles. Therefore, these images are prone to non-uniform illumination, poor contrast, transmissio

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M, Anand, Meenakshi Meenakshi, M. Nandini Rao, N. Varsha H, and Yashaswini H. "An Image Processing Algorithm To Detect Exudates In Fundus Images." Perspectives in Communication, Embedded-systems and Signal-processing - PiCES 6, no. 4 (2022): 19–22. https://doi.org/10.5281/zenodo.6969918.

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Diabetic Retinopathy is often noticed in individuals suffering from diabetes. The major characteristic of this disease is the presence of exudates in the retinal area. These exudates can be scanned using fundus imaging. The aim of the project is to develop an algorithm that can identify exudates in fundus images, and based on the area, prescribe medication using the concept of content-based image retrieval. The algorithm will be developed on MATLAB and also be downloaded on a Spartan 3e FPGA.

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Ramli, Roziana, Khairunnisa Hasikin, Mohd Yamani Idna Idris, Noor Khairiah A. Karim, and Ainuddin Wahid Abdul Wahab. "Fundus Image Registration Technique Based on Local Feature of Retinal Vessels." Applied Sciences 11, no. 23 (2021): 11201. http://dx.doi.org/10.3390/app112311201.

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Feature-based retinal fundus image registration (RIR) technique aligns fundus images according to geometrical transformations estimated between feature point correspondences. To ensure accurate registration, the feature points extracted must be from the retinal vessels and throughout the image. However, noises in the fundus image may resemble retinal vessels in local patches. Therefore, this paper introduces a feature extraction method based on a local feature of retinal vessels (CURVE) that incorporates retinal vessels and noises characteristics to accurately extract feature points on retinal

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Andrikevych, S. A., and S. E. Tuzhanskyi. "Optical fundus image segmentation methods." Optoelectronic Information-Power Technologies 47, no. 1 (2024): 155–65. http://dx.doi.org/10.31649/1681-7893-2024-47-1-155-165.

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The paper presents a comparative analysis and evaluation of methods for segmenting optical fundus images in order to study their efficiency, accuracy, completeness, and computational complexity in Matlab. The methods analyzed are Otsu, adaptive thresholding, Watershed, K-means, maximum expectation algorithm (EM), and Frangi method. The features, advantages and disadvantages in the context of application for the diagnosis of fundus diseases are considered.

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Ward, Nicholas P., Stephen Tomliivson, and Christopher J. Taylor. "Image Analysis of Fundus Photographs." Ophthalmology 96, no. 1 (1989): 80–86. http://dx.doi.org/10.1016/s0161-6420(89)32925-3.

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Lamminen, Heikki. "Picture archiving and fundus imaging in a glaucoma clinic." Journal of Telemedicine and Telecare 9, no. 2 (2003): 114–16. http://dx.doi.org/10.1258/135763303321327993.

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Ophthalmological image archiving and distribution can be automated using a picture archiving and communication system (PACS). A fundus PACS has been in clinical use since February 2000 at the ophthalmology clinic of Tampere University Hospital. It consists of a digital fundus camera, an imaging workstation, from which new patients can be added to the archive, 10 viewing stations and an image archive server. In glaucoma imaging, the fundus images taken from a patient are transferred from the imaging workstation to the image archive server and are then immediately available from the physician's

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Guo, Jifeng, Zhiqi Pang, Fan Yang, Jiayou Shen, and Jian Zhang. "Study on the Method of Fundus Image Generation Based on Improved GAN." Mathematical Problems in Engineering 2020 (July 8, 2020): 1–13. http://dx.doi.org/10.1155/2020/6309596.

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With the continuous development of deep learning, the performance of the intelligent diagnosis system for ocular fundus diseases has been significantly improved, but during the system training process, problems like lack of fundus samples and uneven sample distribution (the number of disease samples is much smaller than the number of normal samples) have become increasingly prominent. In view of the previous issues, this paper proposes a method for generating fundus images based on “Combined GAN” (Com-GAN), which can generate both normal fundus images and fundus images with hard exudates, so t

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Cheng, Lei, Yumeng Li, and Jingyi Han. "Fundus Vascular Segmentation Based on Data Enhancement and Invariant Feature Extraction." Advances in Computer and Engineering Technology Research 1, no. 4 (2024): 30. https://doi.org/10.61935/acetr.4.1.2024.p30.

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Deep neural networks have emerged as the predominant method for medical image segmentation, owing to their robust feature learning capabilities, enabling accurate automatic segmentation of target structures within intricate medical images. Fundus images, being crucial in diagnosing ophthalmic diseases, underscore the importance of effective segmentation techniques. However, fundus vascular images pose challenges due to their high complexity and subtle individual differences, necessitating improvement in existing segmentation methodologies for enhanced disease classification accuracy. This pape

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Mandya Krishnegowda, Prakruthi, and Komarasamy Ganesan. "Modelling on-demand preprocessing framework towards practical approach in clinical analysis of diabetic retinopathy." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (2022): 585. http://dx.doi.org/10.11591/ijece.v12i1.pp585-595.

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<p>Diabetic retinopathy (DR) refers to a complication of diabetes and a prime cause of vision loss in middle-aged people. A timely screening and diagnosis process can reduce the risk of blindness. Fundus imaging is mainly preferred in the clinical analysis of DR. However; the raw fundus images are usually subjected to artifacts, noise, low and varied contrast, which is very hard to process by human visual systems and automated systems. In the existing literature, many solutions are given to enhance the fundus image. However, such approaches are particular and limited to a specific object

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Prakruthi, Mandya Krishnegowda, and Ganesan Komarasamy. "Modelling on-demand preprocessing framework towards practical approach in clinical analysis of diabetic retinopathy." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (2022): 585–95. https://doi.org/10.11591/ijece.v12i1.pp585-595.

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Diabetic retinopathy (DR) refers to a complication of diabetes and a prime cause of vision loss in middle-aged people. A timely screening and diagnosis process can reduce the risk of blindness. Fundus imaging is mainly preferred in the clinical analysis of DR. However; the raw fundus images are usually subjected to artifacts, noise, low and varied contrast, which is very hard to process by human visual systems and automated systems. In the existing literature, many solutions are given to enhance the fundus image. However, such approaches are particular and limited to a specific objective that

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Ko, Ara, and Jungwon Cho. "Ultra-wide-field Fundus Image Synthesis Using Various GAN Models." JOIV : International Journal on Informatics Visualization 6, no. 3 (2022): 618. http://dx.doi.org/10.30630/joiv.6.3.1256.

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Many people lose sight due to diabetic retinopathy. The reason that diabetic retinopathy is dangerous is that it cannot return to its pre-onset state after the disease's onset. Most patients take fundus images that capture the retina, and the doctor uses the fundus images to determine the presence of disease. Existing fundus images could only identify a narrow range, making it difficult to diagnose the disease accurately. However, with technological advances, ultra-wide-field fundus images that allow the wider retina to be seen have emerged. However, in deep learning research, many studies use

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