scholarly journals Automatic Optic Disc Detection in Color Retinal Images by Local Feature Spectrum Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Wei Zhou ◽  
Hao Wu ◽  
Chengdong Wu ◽  
Xiaosheng Yu ◽  
Yugen Yi
Keyword(s):  
Optic Disc ◽  
Spectrum Analysis ◽  
Local Features ◽  
Anatomical Structure ◽  
Local Feature ◽  
Retinal Images ◽  
Selection Approach ◽  
Automated Screening ◽  
Optic Discs ◽  
Screening Systems

The optic disc is a key anatomical structure in retinal images. The ability to detect optic discs in retinal images plays an important role in automated screening systems. Inspired by the fact that humans can find optic discs in retinal images by observing some local features, we propose a local feature spectrum analysis (LFSA) that eliminates the influence caused by the variable spatial positions of local features. In LFSA, a dictionary of local features is used to reconstruct new optic disc candidate images, and the utilization frequencies of every atom in the dictionary are considered as a type of “spectrum” that can be used for classification. We also employ the sparse dictionary selection approach to construct a compact and representative dictionary. Unlike previous approaches, LFSA does not require the segmentation of vessels, and its method of considering the varying information in the retinal images is both simple and robust, making it well-suited for automated screening systems. Experimental results on the largest publicly available dataset indicate the effectiveness of our proposed approach.

scholarly journals Application of Principal Component Analysis in Automatic Localization of Optic Disc and Fovea in Retinal Images

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Asloob Ahmad Mudassar ◽  
Saira Butt
Keyword(s):  
Principal Component Analysis ◽  
Optic Disc ◽  
Retinal Image ◽  
Principal Component ◽  
Component Analysis ◽  
Retinal Images ◽  
Image Area ◽  
Main Components ◽  
Vessel Tree ◽  
Optic Discs

A retinal image has blood vessels, optic disc, fovea, and so forth as the main components of an image. Segmentation of these components has been investigated extensively. Principal component analysis (PCA) is one of the techniques that have been applied to segment the optic disc, but only a limited work has been reported. To our knowledge, fovea segmentation problem has not been reported in the literature using PCA. In this paper, we are presenting the segmentation of optic disc and fovea using PCA. The PCA was trained on optic discs and foveae using ten retinal images and then applied on seventy retinal images with a success rate of 97% in case of optic discs and 94.3% in case of fovea. Conventional algorithms feed one patch at a time from a test retinal image, and the next patch separated by one pixel part is fed. This process is continued till the full image area is covered. This is time consuming. We are suggesting techniques to cut down the processing time with the help of binary vessel tree of a given test image. Results are presented to validate our idea.

scholarly journals Detecting Optic Disc on Asians by Multiscale Gaussian Filtering

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Bob Zhang ◽  
Jane You ◽  
Fakhri Karray
Keyword(s):  
Optic Disc ◽  
Screening Programme ◽  
Matched Filter ◽  
Second Step ◽  
Retinal Images ◽  
Scale Production ◽  
Fundus Images ◽  
Screening Programs ◽  
Gaussian Filtering ◽  
Automated Screening

The optic disc (OD) is an important anatomical feature in retinal images, and its detection is vital for developing automated screening programs. Currently, there is no algorithm designed to automatically detect the OD in fundus images captured from Asians which are larger and have thicker vessels compared to Caucasians. In this paper, we propose such a method to complement current algorithms using two steps: OD vessel candidate detection and OD vessel candidate matching. The first step is achieved with multiscale Gaussian filtering, scale production, and double thresholding to initially extract the vessels' directional map of various thicknesses. The map is then thinned before another threshold is applied to remove pixels with low intensities. This result forms the OD vessel candidates. In the second step, a Vessels' Directional Matched Filter (VDMF) of various dimensions is applied to the candidates to be matched, and the pixel with the smallest difference designated the OD center. We tested the proposed method on a new database consisting of 402 images from a diabetic retinopathy (DR) screening programme consisting of Asians. The OD center was successfully detected with an accuracy of 99.25% (399/402).

Optic Disc Recognition Method for Retinal Images

2015 ◽  
pp. 875-889 ◽  
Author(s):  
Florin Rotaru ◽  
Silviu Ioan Bejinariu ◽  
Cristina Diana Niţă ◽  
Ramona Luca ◽  
Mihaela Luca ◽  
...  
Keyword(s):  
Optic Disc ◽  
Retinal Images ◽  
Recognition Method

Association between glaucomatous optic disc and depressive symptoms independent of light exposure profiles: a cross-sectional study of the HEIJO-KYO cohort

2018 ◽  
Vol 103 (8) ◽  
pp. 1119-1122 ◽  
Author(s):  
Tadanobu Yoshikawa ◽  
Kenji Obayashi ◽  
Kimie Miyata ◽  
Tetsuo Ueda ◽  
Norio Kurumatani ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Optic Disc ◽  
Light Exposure ◽  
Depression Scale ◽  
Cross Sectional Study ◽  
Confounding Factors ◽  
Sectional Study ◽  
Night Time ◽  
Cross Sectional ◽  
Optic Discs

BackgroundGlaucoma may cause physiological and behavioural circadian misalignment because of the loss of intrinsically photosensitive retinal ganglion cells, the primary receptors of environmental light. Although studies have suggested a high prevalence of depression in patients with glaucoma, it is unclear whether the association is independent of the light exposure profiles as an important confounding factor.MethodsIn this cross-sectional study of a community-based cohort of 770 elderly individuals (mean age, 70.9 years), glaucomatous optic discs were assessed using fundus photographs and depressive symptoms were assessed using the short version of the Geriatric Depression Scale (GDS). Daytime and night-time ambient light exposures were objectively measured for 2 days.ResultsDepressive symptoms (GDS score ≥6) were observed in 114 participants (prevalence, 14.8%) and glaucomatous optic discs were detected in 40 participants (prevalence, 5.2%). The prevalence of depressive symptoms was significantly higher in the group with glaucomatous optic disc than in the group without it (30.0% vs 14.0%, respectively; p=0.005). Multivariable logistic regression analysis adjusted for potential confounding factors, including daytime and night-time light exposures, revealed that the OR for depressive symptoms was significantly higher in the group with glaucomatous optic disc than in the group without it (OR 2.45, 95% CI 1.18 to 5.08; p=0.016).ConclusionsIn this general elderly population, glaucomatous optic disc was significantly associated with higher prevalence of depressive symptoms independent of a number of potential confounding factors, including daily light exposure profiles.

scholarly journals Memory-Augmented Transformer for Remote Sensing Image Semantic Segmentation

2021 ◽  
Vol 13 (22) ◽  
pp. 4518
Author(s):  
Xin Zhao ◽  
Jiayi Guo ◽  
Yueting Zhang ◽  
Yirong Wu
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Semantic Segmentation ◽  
Local Features ◽  
Local Feature ◽  
Global Information ◽  
Deep Convolutional Neural Networks ◽  
Global Representation ◽  
Local Feature Extraction ◽  
Global And Local

The semantic segmentation of remote sensing images requires distinguishing local regions of different classes and exploiting a uniform global representation of the same-class instances. Such requirements make it necessary for the segmentation methods to extract discriminative local features between different classes and to explore representative features for all instances of a given class. While common deep convolutional neural networks (DCNNs) can effectively focus on local features, they are limited by their receptive field to obtain consistent global information. In this paper, we propose a memory-augmented transformer (MAT) to effectively model both the local and global information. The feature extraction pipeline of the MAT is split into a memory-based global relationship guidance module and a local feature extraction module. The local feature extraction module mainly consists of a transformer, which is used to extract features from the input images. The global relationship guidance module maintains a memory bank for the consistent encoding of the global information. Global guidance is performed by memory interaction. Bidirectional information flow between the global and local branches is conducted by a memory-query module, as well as a memory-update module, respectively. Experiment results on the ISPRS Potsdam and ISPRS Vaihingen datasets demonstrated that our method can perform competitively with state-of-the-art methods.

scholarly journals Correlation Coefficient Based Automatic Optic Disc Detection from Retinal Images

2020 ◽  
Author(s):  
S. Anand ◽  
R. Prabhadevi ◽  
D. Rini
Keyword(s):  
Optic Disc ◽  
Correlation Coefficient ◽  
Retinal Image ◽  
Retinal Images ◽  
Detection Accuracy ◽  
Maximum Correlation ◽  
Exact Location ◽  
Peak Intensity ◽  
The Given

ABSTRACTIn this paper an algorithm to detect the optic disc (OD) automatically is described. The proposed method is based on the circular brightness of the OD and its correlation coefficient. At first the peak intensity points are taken, a mask is generated for the given image which gives the circular bright regions of the image. To locate the OD accurately, a pattern is generated which is similar to the OD. By correlating the retinal image with the pattern generated, the maximum correlation of the pattern with the OD is obtained. On locating the coordinates of maximum correlation, the exact location of the OD is detected. The proposed algorithm has been tested with DRIVE database images and an average OD detection accuracy of 95% was obtained for healthy and pathological retinas respectively.

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