Detecting Optic Disc on Asians by Multiscale Gaussian Filtering

International Journal of Biomedical Imaging ◽

10.1155/2012/727154 ◽

2012 ◽

Vol 2012 ◽

pp. 1-10 ◽

Cited By ~ 1

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).

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Optic Disc Detection by Multi-scale Gaussian Filtering with Scale Production and a Vessels’ Directional Matched Filter

Lecture Notes in Computer Science - Medical Biometrics ◽

10.1007/978-3-642-13923-9_18 ◽

2010 ◽

pp. 173-180 ◽

Cited By ~ 4

Author(s):

Bob Zhang ◽

Fakhri Karray

Keyword(s):

Optic Disc ◽

Matched Filter ◽

Scale Production ◽

Gaussian Filtering ◽

Multi Scale

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Automatic Grading of Retinal Blood Vessel in Deep Retinal Image Diagnosis

Journal of Medical Systems ◽

10.1007/s10916-020-01635-1 ◽

2020 ◽

Vol 44 (10) ◽

Author(s):

Debasis Maji ◽

Arif Ahmed Sekh

Keyword(s):

Blood Vessels ◽

Machine Learning Algorithms ◽

Retinal Images ◽

Fundus Images ◽

Hard Exudates ◽

Automated Method ◽

Eye Problems ◽

Automated Grading ◽

Automatic Grading ◽

Automated Screening

Abstract Automatic grading of retinal blood vessels from fundus image can be a useful tool for diagnosis, planning and treatment of eye. Automatic diagnosis of retinal images for early detection of glaucoma, stroke, and blindness is emerging in intelligent health care system. The method primarily depends on various abnormal signs, such as area of hard exudates, area of blood vessels, bifurcation points, texture, and entropies. The development of an automated screening system based on vessel width, tortuosity, and vessel branching are also used for grading. However, the automated method that directly can come to a decision by taking the fundus images got less attention. Detecting eye problems based on the tortuosity of the vessel from fundus images is a complicated task for opthalmologists. So automated grading algorithm using deep learning can be most valuable for grading retinal health. The aim of this work is to develop an automatic computer aided diagnosis system to solve the problem. This work approaches to achieve an automatic grading method that is opted using Convolutional Neural Network (CNN) model. In this work we have studied the state-of-the-art machine learning algorithms and proposed an attention network which can grade retinal images. The proposed method is validated on a public dataset EIARG1, which is only publicly available dataset for such task as per our knowledge.

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Optic Disc Detection From Normalized Digital Fundus Images by Means of a Vessels' Direction Matched Filter

IEEE Transactions on Medical Imaging ◽

10.1109/tmi.2007.900326 ◽

2008 ◽

Vol 27 (1) ◽

pp. 11-18 ◽

Cited By ~ 277

Author(s):

Aliaa Abdel-Haleim Abdel-Razik Youssif ◽

Atef Zaki Ghalwash ◽

Amr Ahmed Sabry Abdel-Rahman Ghoneim

Keyword(s):

Optic Disc ◽

Matched Filter ◽

Fundus Images

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

Computational and Mathematical Methods in Medicine ◽

10.1155/2018/1942582 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 2

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.

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

Journal of Electronic Commerce in Organizations ◽

10.4018/jeco.2015040101 ◽

2015 ◽

Vol 13 (2) ◽

pp. 1-13 ◽

Cited By ~ 5

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.

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

The International Arab Journal of Information Technology ◽

10.34028/iajit/16/1/1 ◽

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

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AN EFFICIENT APPROACH TO LOCATE OPTIC DISC CENTER, BLOOD VESSELS AND MACULA IN RETINAL IMAGES

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s101623721250038x ◽

2012 ◽

Vol 24 (05) ◽

pp. 425-434 ◽

Cited By ~ 5

Author(s):

D. Santhi ◽

D. Manimegalai

Keyword(s):

Blood Vessels ◽

Optic Disc ◽

Matched Filter ◽

Geometric Approach ◽

Directional Pattern ◽

Main Step ◽

Retinal Images ◽

Disc Center ◽

Retinal Blood Vessels ◽

Segmentation Methods

Locating the Optic Disc (OD) and Macula Center (MC) is the main step in the automatic extraction of retinal anatomical structures for Diabetic Retinopathy (DR). In this work, a variety of vessel segmentation methods have been adopted for the identification of OD in retinal images. In order to locate OD, different segmentation algorithms like matched filter, spatially weighed Fuzzy C-means and threshold are used to detect the blood vessels from the retinal image. Matching the expected directional pattern of the retinal blood vessels is proposed to locate the OD center. The proposed vessel direction filter is resized and OD center is measured. Macula center (fovea) is located at a constant distance from optic disc. A new method with geometric approach is proposed for macula detection. The proposed methods have been evaluated using a STARE and DRIVE datasets which contains both normal and diseased retina. The proposed methods are successfully adopted for segmentation of blood vessels and location of OD and MC in both normal and abnormal images.

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Two-Stage Mask-RCNN Approach for Detecting and Segmenting the Optic Nerve Head, Optic Disc, and Optic Cup in Fundus Images

Applied Sciences ◽

10.3390/app10113833 ◽

2020 ◽

Vol 10 (11) ◽

pp. 3833 ◽

Cited By ~ 3

Author(s):

Haidar Almubarak ◽

Yakoub Bazi ◽

Naif Alajlan

Keyword(s):

Optic Nerve ◽

Optic Disc ◽

Optic Nerve Head ◽

Fine Tuning ◽

Fundus Images ◽

Two Stage ◽

Second Stage ◽

Retinal Fundus Images ◽

Tuning Strategy ◽

Retinal Fundus

In this paper, we propose a method for localizing the optic nerve head and segmenting the optic disc/cup in retinal fundus images. The approach is based on a simple two-stage Mask-RCNN compared to sophisticated methods that represent the state-of-the-art in the literature. In the first stage, we detect and crop around the optic nerve head then feed the cropped image as input for the second stage. The second stage network is trained using a weighted loss to produce the final segmentation. To further improve the detection in the first stage, we propose a new fine-tuning strategy by combining the cropping output of the first stage with the original training image to train a new detection network using different scales for the region proposal network anchors. We evaluate the method on Retinal Fundus Images for Glaucoma Analysis (REFUGE), Magrabi, and MESSIDOR datasets. We used the REFUGE training subset to train the models in the proposed method. Our method achieved 0.0430 mean absolute error in the vertical cup-to-disc ratio (MAE vCDR) on the REFUGE test set compared to 0.0414 obtained using complex and multiple ensemble networks methods. The models trained with the proposed method transfer well to datasets outside REFUGE, achieving a MAE vCDR of 0.0785 and 0.077 on MESSIDOR and Magrabi datasets, respectively, without being retrained. In terms of detection accuracy, the proposed new fine-tuning strategy improved the detection rate from 96.7% to 98.04% on MESSIDOR and from 93.6% to 100% on Magrabi datasets compared to the reported detection rates in the literature.

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