Automatic Localization of the Optic Disc Center in Retinal Images based on Angle Detection in Curvature Scale Space

2015 ◽  
Vol 13 (2) ◽  
pp. 1-13 ◽  
Author(s):  
A. Elbalaoui ◽  
Mohamed Fakir ◽  
M. Boutaounte ◽  
A. Merbouha
Keyword(s):  
Diabetic Retinopathy ◽  
Optic Disc ◽  
Retinal Image ◽  
Scale Space ◽  
Second Step ◽  
Retinal Images ◽  
Anatomical Structures ◽  
Disc Center ◽  
Curvature Scale Space ◽  
Automatic Localization

Digital images of the retina is widely used for screening of patients suffering from sight threatening diseases such as Diabetic retinopathy and Glaucoma. The localization of the Optic Disc (OD) center is the first and necessary step identification and segmentation of anatomical structures and in pathological retinal images. From the center of the optic disc spreads the major blood vessels of the retina. Therefore, by considering the high number of vessels and the high number of the angles resulted from the vessels crossing, the authors propose a new method based on the number of angles in the vicinity of optic disc for localization of the center of optic disc. The first step is pre-processing of retinal image for separate the fundus from its background and increase the contrast between contours. In the second step, the authors use the Curvature Scale Space (CSS) for angle detection. In the next step, they move a window about the size of optic disc to count the number of corners. In the final step, they use the center of windows which has the most number of corners for localizing the optic disc center. The proposed method is evaluated on DRIVE, CHASE_DB1 and STARE databases and the success rate is 100, 100 and 96.3%, respectively.

Automatic Localization of the Optic Disc Center in Retinal Images Based on Angle Detection in Curvature Scale Space

2017 ◽  
pp. 679-692
Author(s):  
A. Elbalaoui ◽  
Mohamed Fakir ◽  
M. Boutaounte ◽  
A. Merbouha
Keyword(s):  
Diabetic Retinopathy ◽  
Optic Disc ◽  
Digital Images ◽  
Scale Space ◽  
New Method ◽  
Retinal Images ◽  
Anatomical Structures ◽  
Disc Center ◽  
Curvature Scale Space ◽  
Automatic Localization

Digital images of the retina is widely used for screening of patients suffering from sight threatening diseases such as Diabetic retinopathy and Glaucoma. The localization of the Optic Disc (OD) center is the first and necessary step identification and segmentation of anatomical structures and in pathological retinal images. From the center of the optic disc spreads the major blood vessels of the retina. Therefore, by considering the high number of vessels and the high number of the angles resulted from the vessels crossing, the authors propose a new method based on the number of angles in the vicinity of optic disc for localization of the center of optic disc. The first step is pre-processing of retinal image for separate the fundus from its background and increase the contrast between contours. In the second step, the authors use the Curvature Scale Space (CSS) for angle detection. In the next step, they move a window about the size of optic disc to count the number of corners. In the final step, they use the center of windows which has the most number of corners for localizing the optic disc center. The proposed method is evaluated on DRIVE, CHASE_DB1 and STARE databases and the success rate is 100, 100 and 96.3%, respectively.

scholarly journals Localization and segmentation of optic disc in retinal images using Circular Hough transform and Grow Cut algorithm

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2003 ◽  
Author(s):  
Muhammad Abdullah ◽  
Muhammad Moazam Fraz ◽  
Sarah A. Barman
Keyword(s):  
Optic Disc ◽  
Hough Transform ◽  
Retinal Image ◽  
Diagnostic System ◽  
Retinal Images ◽  
Computer Assisted ◽  
Morphological Operations ◽  
Circular Hough Transform ◽  
Disc Center ◽  
Unique Method

Automated retinal image analysis has been emerging as an important diagnostic tool for early detection of eye-related diseases such as glaucoma and diabetic retinopathy. In this paper, we have presented a robust methodology for optic disc detection and boundary segmentation, which can be seen as the preliminary step in the development of a computer-assisted diagnostic system for glaucoma in retinal images. The proposed method is based on morphological operations, the Circular Hough transform and the Grow Cut algorithm. The morphological operators are used to enhance the optic disc and remove the retinal vasculature and other pathologies. The optic disc center is approximated using the Circular Hough transform, and the Grow Cut algorithm is employed to precisely segment the optic disc boundary. The method is quantitatively evaluated on five publicly available retinal image databases DRIVE, DIARETDB1, CHASE_DB1, DRIONS-DB, Messidor and one local Shifa Hospital Database. The method achieves an optic disc detection success rate of 100% for these databases with the exception of 99.09% and 99.25% for the DRIONS-DB, Messidor, and ONHSD databases, respectively. The optic disc boundary detection achieved an average spatial overlap of 78.6%, 85.12%, 83.23%, 85.1%, 87.93%, 80.1%, and 86.1%, respectively, for these databases. This unique method has shown significant improvement over existing methods in terms of detection and boundary extraction of the optic disc.

scholarly journals Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Kemal Akyol ◽  
Baha Şen ◽  
Şafak Bayır
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Texture Analysis ◽  
Optic Disc ◽  
Retinal Image ◽  
Disease Diagnosis ◽  
Retinal Images ◽  
Hard Exudate ◽  
Visual Dictionary ◽  
Public Datasets

With the advances in the computer field, methods and techniques in automatic image processing and analysis provide the opportunity to detect automatically the change and degeneration in retinal images. Localization of the optic disc is extremely important for determining the hard exudate lesions or neovascularization, which is the later phase of diabetic retinopathy, in computer aided eye disease diagnosis systems. Whereas optic disc detection is fairly an easy process in normal retinal images, detecting this region in the retinal image which is diabetic retinopathy disease may be difficult. Sometimes information related to optic disc and hard exudate information may be the same in terms of machine learning. We presented a novel approach for efficient and accurate localization of optic disc in retinal images having noise and other lesions. This approach is comprised of five main steps which are image processing, keypoint extraction, texture analysis, visual dictionary, and classifier techniques. We tested our proposed technique on 3 public datasets and obtained quantitative results. Experimental results show that an average optic disc detection accuracy of 94.38%, 95.00%, and 90.00% is achieved, respectively, on the following public datasets: DIARETDB1, DRIVE, and ROC.

scholarly journals An Automatic Localization of Optic Disc in Low Resolution Retinal Images by Modified Directional Matched Filter

2018 ◽  
Vol 16 (1) ◽  
pp. 1-7
Author(s):  
Murugan Raman ◽  
Reeba Korah ◽  
Kavitha Tamilselvan
Keyword(s):  
Diabetic Retinopathy ◽  
Optic Disc ◽  
Matched Filter ◽  
Retinal Images ◽  
Low Resolution ◽  
Automated Method ◽  
Retinal Blood Vessels ◽  
Optic Disc Localization ◽  
Automatic Localization ◽  
Success Percentage

An automatic optic disc localization in retinal images used to screen eye related diseases like diabetic retinopathy. Many techniques are available to detect Optic Disc (OD) in high-resolution retinal images. Unfortunately, there are no efficient methods available to detect OD in low-resolution retinal images. The objective of this research paper is to develop an automated method for localization of Optic Disc in low resolution retinal images. This paper proposes a modified directional matched filter parameters of the retinal blood vessels to localize the center of optic disc. The proposed method was implemented in MATLAB and evaluated both normal and abnormal low resolution retinal images using the subset of Optic Nerve Head Segmentation Dataset (ONHSD) and the success percentage was found to be an average of 96.96% with 23seconds

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.

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 Contrast based circular approximation for accurate and robust optic disc segmentation in retinal images

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3763 ◽  
Author(s):  
Jose Sigut ◽  
Omar Nunez ◽  
Francisco Fumero ◽  
Marta Gonzalez ◽  
Rafael Arnay
Keyword(s):  
Optic Disc ◽  
Detection Rate ◽  
State Of The Art ◽  
Retinal Images ◽  
Disc Center ◽  
Circular Model ◽  
Starting Point ◽  
Optic Disc Localization ◽  
Average Contrast ◽  
Overlap Coefficient

A new method for automatic optic disc localization and segmentation is presented. The localization procedure combines vascular and brightness information to provide the best estimate of the optic disc center which is the starting point for the segmentation algorithm. A detection rate of 99.58% and 100% was achieved for the Messidor and ONHSD databases, respectively. A simple circular approximation to the optic disc boundary is proposed based on the maximum average contrast between the inner and outer ring of a circle centered on the estimated location. An average overlap coefficient of 0.890 and 0.865 was achieved for the same datasets, outperforming other state of the art methods. The results obtained confirm the advantages of using a simple circular model under non-ideal conditions as opposed to more complex deformable models.

Localization of the optic disc center in retinal images based on the Harris corner detector

2012 ◽  
Vol 2 (3) ◽  
pp. 198-206 ◽  
Author(s):  
Amin Dehghani ◽  
Mohammad-Shahram Moin ◽  
Mohammad Saghafi
Keyword(s):  
Optic Disc ◽  
Retinal Images ◽  
Disc Center ◽  
Harris Corner ◽  
Harris Corner Detector

scholarly journals Automatic Detection of Optic Disc for Diabetic Retinopathy

2020 ◽  
Vol 9 (7) ◽  
pp. 1013-1015
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Optic Disc ◽  
Early Stage ◽  
Vital Role ◽  
Retinal Images ◽  
Image Processing Techniques ◽  
Canny Edge ◽  
Total Blindness ◽  
Processing Techniques

Diabetic Retinopathy affects the retina of the eye and eventually it may lead to total visual impairment. Total blindness can be avoided by detecting Diabetic Retinopathy at an early stage. Various manual tests are used by the doctors to detect the presence of disease, but they are tedious and expensive. Some of the features of Diabetic Retinopathy are exudates, haemorrhages and micro aneurysms. Detection and removal of optic disc plays a vital role in extraction of these features. This paper focuses on detection of optic disc using various image processing techniques, algorithms such as Canny edge, Circular Hough (CHT). Retinal images from IDRiD, Diaret_db0, Diaret_db1, Chasedb and Messidor datasets were used.

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