scholarly journals FIRE: Fundus Image Registration dataset

2017 ◽  
Vol 1 (4) ◽  
pp. 16-28
Author(s):  
Carlos Hernandez-Matas ◽  
Xenophon Zabulis ◽  
Areti Triantafyllou ◽  
Panagiota Anyfanti ◽  
Stella Douma ◽  
...  
Keyword(s):  
Image Registration ◽  
Retinal Image ◽  
Ground Truth ◽  
Fundus Image ◽  
Registration Method ◽  
Evaluation Protocol ◽  
Retinal Fundus Image ◽  
Fair Comparison ◽  
Image Pairs ◽  
Retinal Fundus

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 between images and the presence/absence of anatomical differences. Ground truth in the form of corresponding image points and a protocol to evaluate registration performance are provided.Results: The proposed protocol is shown to enable quantitative and comparative evaluation of retinal registration methods under a variety of conditions.Conclusion: This work enables the fair comparison of retinal registration methods. It also helps researchers to select the registration method that is most appropriate given a specific target use.

scholarly journals Retinal Fundus Image Enhancement Using the Normalized Convolution and Noise Removing

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Peishan Dai ◽  
Hanwei Sheng ◽  
Jianmei Zhang ◽  
Ling Li ◽  
Jing Wu ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Retinal Image ◽  
Median Filter ◽  
Basic Information ◽  
Fundus Image ◽  
Denoising Method ◽  
Convolution Algorithm ◽  
Retinal Fundus Image ◽  
Normalized Convolution ◽  
Retinal Fundus

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 processed by the normalized convolution algorithm with a domain transform to obtain an image with the basic information of the background. Then, the image with the basic information of the background was fused with the original retinal fundus image to obtain an enhanced fundus image. Lastly, the fused image was denoised by a two-stage denoising method including the fourth order PDEs and the relaxed median filter. The retinal image databases, including the DRIVE database, the STARE database, and the DIARETDB1 database, were used to evaluate image enhancement effects. The results show that the method can enhance the retinal fundus image prominently. And, different from some other fundus image enhancement methods, the proposed method can directly enhance color images.

Two-stage Retinal Fundus Image Registration Based on Local Blood Structure Features

2012 ◽  
Vol 41 (10) ◽  
pp. 1236-1241
Author(s):  
沈奔 SHEN Ben ◽  
张东波 ZHANG Dong-bo ◽  
彭英辉 PENG Ying-hui
Keyword(s):  
Image Registration ◽  
Fundus Image ◽  
Two Stage ◽  
Retinal Fundus Image ◽  
Retinal Fundus

scholarly journals Local descriptor for retinal fundus image registration

2020 ◽  
Vol 14 (4) ◽  
pp. 144-153
Author(s):  
Roziana Ramli ◽  
Mohd Yamani Idna Idris ◽  
Khairunnisa Hasikin ◽  
Noor Khairiah A. Karim ◽  
Ainuddin Wahid Abdul Wahab ◽  
...  
Keyword(s):  
Image Registration ◽  
Fundus Image ◽  
Local Descriptor ◽  
Retinal Fundus Image ◽  
Retinal Fundus

scholarly journals Optic Cup Segmentation using U-Net Architecture on Retinal Fundus Image

2020 ◽  
Vol 4 (02) ◽  
pp. 105-109
Author(s):  
Pulung Hendro Prastyo ◽  
Amin Siddiq Sumi ◽  
Annis Nuraini
Keyword(s):  
Ground Truth ◽  
Batch Size ◽  
Fundus Images ◽  
Fundus Image ◽  
Dice Coefficient ◽  
Input Size ◽  
Retinal Fundus Image ◽  
Optic Cup ◽  
Retinal Fundus Images ◽  
Retinal Fundus

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 Entropy. We employed the Dice Coefficient as the evaluator. Besides, the segmentation results were compared to the ground truth images. According to the experimental results, the performance of optic cup segmentation achieved 98.42% for the Dice coefficient and loss of 1,58%. These results implied that our proposed method succeeded in segmenting the optic cup on color retinal fundus images.

scholarly journals Determination of cup-to-disc ratio of optical nerve head for diagnosis of glaucoma on stereo retinal fundus image pairs

2009 ◽  
Author(s):  
Chisako Muramatsu ◽  
Toshiaki Nakagawa ◽  
Akira Sawada ◽  
Yuji Hatanaka ◽  
Takeshi Hara ◽  
...  
Keyword(s):  
Fundus Image ◽  
Retinal Fundus Image ◽  
Image Pairs ◽  
Retinal Fundus ◽  
Cup To Disc Ratio
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