A Novel Approach to Method for the Detection of Optic Disc Location in Retinal Images using Image Processing Techniques (original) (raw)
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Automated localisation of retinal optic disk using Hough transform
2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008
The retinal fundus photograph is widely used in the diagnosis and treatment of various eye diseases such as diabetic retinopathy and glaucoma. Medical image analysis and processing has great significance in the field of medicine, especially in non-invasive treatment and clinical study. Normally fundus images are manually graded by specially trained clinicians in a time-consuming and resource-intensive process. A computer-aided fundus image analysis could provide an immediate detection and characterisation of retinal features prior to specialist inspection. This paper describes a novel method to automatically localise one such feature: the optic disk. The proposed method consists of two steps: in the first step, a circular region of interest is found by first isolating the brightest area in the image by means of morphological processing, and in the second step, the Hough transform is used to detect the main circular feature (corresponding to the optical disk) within the positive horizontal gradient image within this region of interest. Initial results on a database of fundus images show that the proposed method is effective and favourable in relation to comparable techniques.
Detection of Optic Disc in Retina Using Hough Transform
INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY
We propose a method to automatically locate the Optic Disc (OD) in fundus images of the retina. Based on the properties of the OD, our proposed method includes edge detection using the Canny method, and detection of circles using the Hough transform. The Hough transform assists in the detection of the center and radius of a circle that approximates the margin of the OD. Based on the feature that the OD is one of the brightest areas in fundus image, the potential circles can be detected by Hough transform.
Detection of Optic Disc Centre Point in Retinal Image
Journal of Electrical Technology UMY, 2019
Glaucoma and diabetic retinopathy (DR) are the two most common retinal related diseases occurred in the world. Glaucoma can be diagnosed by measuring optic cup to disc ratio (CDR) defined as optic cup to optic disc vertical diameter ratio of retinal fundus image. A computer based optic disc is expected to assist the ophthalmologist to find their location which are necessary for glaucoma and DR diagnosis. However, many optic disc detection algorithms available now are commonly non-automatic and only work in healthy retinal image. Therefore, there is not information on how optic disc in retinal image of unhealthy patient can be extracted computationally. In this research work, the method for automated detection of optic disc on retinal colour fundus images has been developed to facilitate and assist ophthalmologists in the diagnosis of retinal related diseases. The results indicated that the proposed method can be implemented in computer aided diagnosis of glaucoma and diabetic retinopathy system development.
An Automatic Screening Method to Detect Optic Disc in the Retina
International Journal of Advanced Information Technology, 2012
The location of Optic Disc (OD) is of critical importance in retinal image analysis. This research paper carries out a new automated methodology to detect the optic disc (OD) in retinal images. OD detection helps the ophthalmologists to find whether the patient is affected by diabetic retinopathy or not. The proposed technique is to use line operator which gives higher percentage of detection than the already existing methods. The purpose of this project is to automatically detect the position of the OD in digital retinal fundus images. The method starts with converting the RGB image input into its LAB component. This image is smoothed using bilateral smoothing filter. Further, filtering is carried out using line operator. After which gray orientation and binary map orientation is carried out and then with the use of the resulting maximum image variation the area of the presence of the OD is found. The portions other than OD are blurred using 2D circular convolution. On applying mathematical steps like peak classification, concentric circles design and image difference calculation, OD is detected. The proposed method was evaluated using a subset of the STARE project's dataset and the success percentage was found to be 96%.
Diabetic Retinopathy Detection using Hough Transform and Bottom Hat Transform
Now a days computer aided design and diagnosis is very popular. Most of the diseases screening and detection is performed with the help of a computer. Diabetic retinopathy is one of the diabetic eye diseases found in the patients who have diabetic in last 20-30 years. The main objective of this work is to effectively found diabetic retinopathy those who have diabetic by using Hough transform and bottom hat transform. Selection of the needed region and extract the decided feature is very important in CAD. Hough transform is one of the best method for feature extraction. It follows voting procedure for feature extraction. The purpose of the technique is to find imperfect instances of objects within a certain class of shapes.
PeerJ, 2016
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 ach...
Hybrid Method based Retinal Optic Disc Detection
We propose a hybrid method based for the Optic Disc (OD) detection in the retinal image. This research consists of three main steps. First, blood vessel removal with homomorphic and median filtering. Second, edge detection using canny operator. Third, OD detection using the Hough transform. The Hough transform is used since the objective object is the curve with circle shape, i.e. optic disc region. Therefore, we can find the shape by using the Hough transform with the circle equation. In this research, the generated circles from Hough transform are matched with the edge pixels of the retinal image. The closest match (showed by the maximum value of accumulator) means that the optic disc is detected. The experiments show that the best accuracy is achieved when the distance value between the generated circles is 3. The average sensitivity, specificity, and balanced accuracy are 64.6182575%, 98.58545%, and 81.6018%, respectively.
In this paper, a new automated methodology to detect the optic disc (OD) automatically in retinal images from patients with risk of being affected by Diabetic Retinopathy (DR) and Macular Edema (ME) is presented. The detection procedure comprises two independent methodologies. On one hand, a location methodology obtains a pixel that belongs to the OD using image contrast analysis and structure filtering techniques and, on the other hand, a boundary segmentation methodology estimates a circular approximation of the OD boundary by applying mathematical morphology, edge detection techniques and the Circular Hough Transform. The methodologies were tested on a set of 1200 images composed of 229 retinographies from patients affected by DR with risk of ME, 431 with DR and no risk of ME and 540 images of healthy retinas. The location methodology obtained 98.83% success rate, whereas the OD boundary segmentation methodology obtained good circular OD boundary approximation in 94.58% of cases. The average computational time measured over the total set was 1.67 seconds for OD location and 5.78 seconds for OD boundary segmentation.
Retinal Images: Optic Disk Localization and Detection
Lecture Notes in Computer Science, 2010
Automated localization and detection of the optic disc (OD) is an essential step in the analysis of digital diabetic retinopathy systems. Accurate localization and detection of optic disc boundary is very useful in proliferative diabetic retinopathy where fragile vessels develop in the retina. In this paper, we propose an automated system for optic disk localization and detection. Our method localizes optic disk using average filter and thresholding, extracts the region of interest (ROI) containing optic disk to save time and detects the optic disk boundary using Hough transform. This method can be used in computerized analysis of retinal images, e.g., in automated screening for diabetic retinopathy. The technique is tested on publicly available DRIVE, STARE, diaretdb0 and diaretdb1 databases of manually labeled images which have been established to facilitate comparative studies on localization and detection of optic disk in retinal images. The proposed method achieves an average accuracy of 96.7% for localization and an average area under the receiver operating characteristic curve of 0.958 for optic detection.
SN Applied Sciences
Detection of the optic disc which has similar brightness with the hard and soft exudate lesions seen in the early stage of diabetic retinopathy is very difficult due to different light conditions and contrast values. Automatic detection of these lesions by expert systems in the medical field is very important. In this context, we propose a new approach based on the analysis of color spaces, keypoint detectors, and texture for retinal fundus images. If the keypoint information is contained within the actual optic disc region, this is an important consideration for the automated detection of the optic disc. This study can be divided into five sections, respectively, image preprocessing, image processing, keypoint detection, texture analysis, and performance evaluation. The analyses of patch images compatible with the keypoints obtained from the Red–Green–Blue (RGB) image and its color channels were carried out. The performance of the study was validated on the Digital Retinal Images f...