scholarly journals Comparisons of Effective Fields of Two Ultra-Widefield Ophthalmoscopes, Optos 200Tx and Clarus 500

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Yoshitsugu Matsui ◽  
Atsushi Ichio ◽  
Asako Sugawara ◽  
Eriko Uchiyama ◽  
Hitomi Suimon ◽  
...  
Keyword(s):  
Retinal Imaging ◽  
Retinal Vessel ◽  
Effective Field ◽  
Eye Diseases ◽  
The Other ◽  
Retinal Images ◽  
Single Image ◽  
Depth Of Focus ◽  
Temporal Quadrant

Purpose. To compare the effective fields of the Optos 200Tx® and Clarus 500™, two ultra-widefield ophthalmoscopes, based on their ability to image branches of retinal vessel in the four retinal quadrants. Methods. Ninety retinal images from 90 patients with various eye diseases were studied. All patients had undergone 200° retinal imaging to obtain a single image of Optos (O) and the montage of two images of the Clarus (C). The highest number of traceable vessel branches in the four retinal quadrants was determined by two masked raters. An image was classified as “O > C” when the number of identifiable branch was greater in the Optos than the Clarus, as “O = C” when the number was equal and as “O < C” when the number was fewer in the Optos than the Clarus. Results. The appearance probability of “O > C” was significantly higher at the upper temporal quadrant than “O < C” (p<0.01 for both raters). In contrast, the appearance probability of “O < C” was significantly higher at the lower nasal quadrant than “O > C” (p<0.01 for both raters). There were no significant differences in the appearance probability between “O > C” and “O < C” at the other two retinal quadrants (p>0.50 for both raters). Conclusions. These results demonstrate that the effective field of views was different between the two devices at different retina quadrants. Further studies are needed to clarify possible factors such as artifacts by the eyelashes, differences in the depth of focus, motion of the device, and different locations of the images on the effective field of views.

scholarly journals Retinal Image Enhancement Based on Contrast, Luminosity Adjustment and MSC

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2572-2574
Keyword(s):  
Image Enhancement ◽  
Sparse Coding ◽  
Retinal Vessel ◽  
Poor Quality ◽  
The Other ◽  
Retinal Images ◽  
Retinal Diseases ◽  
Medical Studies ◽  
Basic Quality

Retinal images have been widely used by ophthalmologists for detecting the retinal diseases before-hand and diagnosing them suitably. Old age macular degeneration, diabetic retinopathy and glaucoma are some examples of these diseases. However, poor quality of the image due to inadvertent circumstances limits the ability of the ophthalmologists to study the image. This paper hereby proposes an algorithm that is used to obtain clearer images by performing contrast and luminosity adjustment that enhances the basic quality of the clicked image. Following this, Multi-dictionary Sparse Coding (MSC) is carried out on the image to obtain the retinal vessel structures and miniscule details. Amount of Image enhancement is calculated by measuring the improvement after each stage of operation on the image. The image's quality is found to be much better compared to the other methods and thus can be suggested to the ophthalmologists for conducting the further medical studies conveniently.

scholarly journals Blur Discrimination and its Relation to Blur-Mediated Depth Perception

Perception ◽  
10.1068/p3254 ◽  
2002 ◽  
Vol 31 (10) ◽  
pp. 1211-1219 ◽  
Author(s):  
George Mather ◽  
David R R Smith
Keyword(s):  
Depth Perception ◽  
Three Dimensional ◽  
The Other ◽  
Retinal Images ◽  
Apparent Depth ◽  
Depth Of Focus ◽  
Depth Cue ◽  
Series Of Experiments ◽  
Different Levels ◽  
Depth Ordering

Retinal images of three-dimensional scenes often contain regions that are spatially blurred by different amounts, owing to depth variation in the scene and depth-of-focus limitations in the eye. Variations in blur between regions in the retinal image therefore offer a cue to their relative physical depths. In the first experiment we investigated apparent depth ordering in images containing two regions of random texture separated by a vertical sinusoidal border. The texture was sharp on one side of the border, and blurred on the other side. In some presentations the border itself was also blurred. Results showed that blur variation alone is sufficient to determine the apparent depth ordering. A subsequent series of experiments measured blur-discrimination thresholds with stimuli similar to those used in the depth-ordering experiment. Weber fractions for blur discrimination ranged from 0.28 to 0.56. It is concluded that the utility of blur variation as a depth cue is constrained by the relatively mediocre ability of observers to discriminate different levels of blur. Blur is best viewed as a relatively coarse, qualitative depth cue.

Continuous Motion Classification and Segmentation Based on Improved Dynamic Time Warping Algorithm

2017 ◽  
Vol 32 (02) ◽  
pp. 1850002 ◽  
Author(s):  
Mingqin Liu ◽  
Xiaoguang Zhang ◽  
Guiyun Xu
Keyword(s):  
Action Recognition ◽  
Dynamic Time Warping ◽  
The Other ◽  
Continuous Image ◽  
Single Image ◽  
Time Warping ◽  
Continuous Action ◽  
Template Sequence ◽  
Dynamic Time

The continuous image sequence recognition is more difficult than the single image recognition because the classification of continuous image sequences and the image edge recognition must be very accurate. Hence, a method based on sequence alignment for action segmentation and classification is proposed to reconstruct a template sequence by estimating the mean action of a class category, which calculates the distance between a single image and a template sequence by sparse coding in Dynamic Time Warping. The proposed method, the methods of Kulkarni et al. [Continuous action recognition based on sequence alignment, Int. J. Comput. Vis. pp. 1–26.] and Hoai et al. [Joint segmentation and classification of human actions in video, IEEE Conf. Computer Vision and Pattern Recognition, 2008, pp. 108–119.] are compared on the recognition accuracy of the continuous recognition and isolated recognition, which clearly shows that the proposed method outperforms the other methods. When applied to continuous gesture classification, it not only can recognize the gesture categories more quickly and accurately, but is more realistic in solving continuous action recognition problems in a video than the other existing methods.

scholarly journals Tortuosity Index Calculations in Retinal Images: Some Criticalities Arising from Commonly Used Approaches

Information ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 466
Author(s):  
Francesco Martelli ◽  
Claudia Giacomozzi
Keyword(s):  
Retinal Imaging ◽  
Retinal Images ◽  
Cardiovascular Research ◽  
Vascular Tortuosity ◽  
Retinal Microvasculature ◽  
Growing Body

A growing body of research in retinal imaging is recently considering vascular tortuosity measures or indexes, with definitions and methods mostly derived from cardiovascular research. However, retinal microvasculature has its own peculiarities that must be considered in order to produce reliable measurements. This study analyzed and compared various derived metrics (e.g., TI, TI_avg, TI*CV) across four existing computational workflows. Specifically, the implementation of the models on two critical OCT images highlighted main pitfalls of the methods, which may fail in reliably differentiating a highly tortuous image from a normal one. A tentative, encouraging approach to mitigate the issue on the same OCT exemplificative images is described in the paper, based on the suggested index TI*CV.

Trakumn in eye practice

1926 ◽  
Vol 22 (1) ◽  
pp. 106
Author(s):  
V. A.
Keyword(s):  
Eye Diseases ◽  
The Other ◽  
Copper Treatment ◽  
Better Than

Grimingen (Zeit. F. Augenheilk., 1925, Bd. 55) reports on a new preparation of copper - tracumin, which, in the form of a 5% ointment, proved to work better than all the other methods of copper treatment known so far for eye diseases and in particular with trachoma, both complicated by corneal lesions and not complicated. Good results from tracumin were obtained by the author and with pannus, as well as with eczematous pannus and follicular catarrh.

scholarly journals Retinal vessel feature extraction from fundus image using image processing techniques

2018 ◽  
Vol 7 (2) ◽  
pp. 687
Author(s):  
R. Lavanya ◽  
G. K. Rajini ◽  
G. Vidhya Sagar
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Early Detection ◽  
Blood Vessels ◽  
Retinal Vessel ◽  
Retinal Images ◽  
Image Processing Techniques ◽  
Vessel Detection ◽  
Proper Diagnosis ◽  
Processing Techniques

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 vessels in the retina become weakened and leak, forming small hemorrhages. As the disease progress, blood vessels may block, and sometimes leads to permanent vision loss. To help Clinicians in diagnosis of diabetic retinopathy in retinal images with an early detection of abnormalities with automated tools.Methods: Fundus photography is an imaging technology used to capture retinal images in diabetic patient through fundus camera. Adaptive Thresholding is used as pre-processing techniques to increase the contrast, and filters are applied to enhance the image quality. Morphological processing is used to detect the shape of blood vessels as they are nonlinear in nature.Results: Image features like, Mean and Standard deviation and entropy, for textural analysis of image with Gray Level Co-occurrence Matrix features like contrast and Energy are calculated for detected vessels.Conclusion: In diabetic patients eyes are affected severely compared to other organs. Early detection of vessel structure in retinal images with computer assisted tools may assist Clinicians for proper diagnosis and pathology. 

A New Method to Characterize Dislocation Loops

1994 ◽  
Vol 375 ◽  
Author(s):  
Fuping Liu ◽  
Ian Baker ◽  
Michael Dudley
Keyword(s):  
The Other ◽  
New Method ◽  
Beam Divergence ◽  
Dislocation Loops ◽  
Single Image ◽  
Double Image ◽  
Strain Fields ◽  
X Ray ◽  
White Beam ◽  
Theoretical Analyses

AbstractWhite-beam synchrotron X-ray topography has been used to study the circular, prismatic, [0001] dislocation loops which are commonly-observed on the (0001) plane in polycrystalline, freshwater ice. A new method, involving detailed analyses of the effects of beam divergence on the loop images, has been developed to determine whether a loop is of vacancy or interstitial type. In an 0002 image, one half of a loop (projected as an ellipse) appears as a single image and the other half as a double image. Experimentally, it was found that the 0002 vector drawn from the center of a loop passes through the single image if the loop is of vacancy-type and through the double image if a loop is of interstitial-type. This method of loop characterization was confirmed by performing theoretical analyses of both the dislocation image widths and their strain fields.

Single image haze removal based on fusion darkness channel prior

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740037 ◽  
Author(s):  
Xifang Zhu ◽  
Ruxi Xiang ◽  
Feng Wu ◽  
Xiaoyan Jiang
Keyword(s):  
Image Quality ◽  
Channel Model ◽  
Input Image ◽  
The Other ◽  
Fusion Method ◽  
Single Image ◽  
Atmospheric Scattering ◽  
Haze Removal ◽  
Correlation Information ◽  
Atmospheric Scattering Model

To improve the image quality and compensate deficiencies of haze removal, we presented a novel fusion method. By analyzing the darkness channel of each method, the effective darkness channel model that takes the correlation information of each darkness channel into account was constructed. This method was used to estimate the transmission map of the input image, and refined by the modified guided filter in order to further improve the image quality. Finally, the radiance image was restored by combining the monochrome atmospheric scattering model. Experimental results show that the proposed method not only effectively remove the haze of the image, but also outperform the other haze removal methods.

scholarly journals A Two-Stage GAN for High-Resolution Retinal Image Generation and Segmentation

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 60
Author(s):  
Paolo Andreini ◽  
Giorgio Ciano ◽  
Simone Bonechi ◽  
Caterina Graziani ◽  
Veronica Lachi ◽  
...  
Keyword(s):  
High Resolution ◽  
Retinal Vessel ◽  
Real Data ◽  
Generative Adversarial Networks ◽  
Retinal Images ◽  
Two Stage ◽  
Generation Task ◽  
Training Samples ◽  
Vessel Structure ◽  
Semantic Label

In this paper, we use Generative Adversarial Networks (GANs) to synthesize high-quality retinal images along with the corresponding semantic label-maps, instead of real images during training of a segmentation network. Different from other previous proposals, we employ a two-step approach: first, a progressively growing GAN is trained to generate the semantic label-maps, which describes the blood vessel structure (i.e., the vasculature); second, an image-to-image translation approach is used to obtain realistic retinal images from the generated vasculature. The adoption of a two-stage process simplifies the generation task, so that the network training requires fewer images with consequent lower memory usage. Moreover, learning is effective, and with only a handful of training samples, our approach generates realistic high-resolution images, which can be successfully used to enlarge small available datasets. Comparable results were obtained by employing only synthetic images in place of real data during training. The practical viability of the proposed approach was demonstrated on two well-established benchmark sets for retinal vessel segmentation—both containing a very small number of training samples—obtaining better performance with respect to state-of-the-art techniques.

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