Automatic Detection of Optic Disc and Optic Cup using Simple Linear Iterative Clustering (original) (raw)
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IJERT-Automatic Detection of Optic Disc and Optic Cup using Simple Linear Iterative Clustering
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/automatic-detection-of-optic-disc-and-optic-cup-using-simple-linear-iterative-clustering https://www.ijert.org/research/automatic-detection-of-optic-disc-and-optic-cup-using-simple-linear-iterative-clustering-IJERTV3IS070947.pdf The retinal optic disc is the region from where the central retinal artery and optical nerve of the retina emanate. Hence, it often serves as an important landmark and reference for other features in a retinal fundus image. The features obtained from a fundus images are often helpful in the diagnosis of various eye diseases. Locating and segmenting the optic disc are key pre-processing steps for extracting retinal features. The manual examination of optic disc (OD) is a standard procedure used for detecting eye diseases. In this paper, we present an automatic Optic disc detection technique based on simple linear iterative clustering. The method proposed can be used for the segmentation of optic cup. Principal component analysis and mathematical morphology is performed to prepare the image for segmentation.
A Simple Method for Optic Disk Segmentation from Retinal Fundus Image
—Detection of optic disc area is complex because it is located in an area that is considered as pathological blood vessels when in segmentation and thus require a method to detect the area of the optic disc, this paper proposed the optic disc segmentation using a method that has not been used before, and this method is very simple, K-means clustering is a proposed Method in this paper to detect the optic disc area with perfected using adaptive morphology. This paper successfully detect optic disc area quickly and segmented blood vessels more quickly.
Automated Segmentation of Optic Disc and Cup in Color Fundus Images
Annals of Advanced Biomedical Sciences, 2019
An automatic Optic disc and Optic cup detection technique which is an important step in developing systems for computer-aided eye disease diagnosis is presented in this paper. This paper presents an algorithm for localization and segmentation of optic disc from digital retinal images. OD localization is achieved by circular Hough transform using morphological preprocessing and segmentation is achieved by watershed transformation. Optic cup segmentation is achieved by marker controlled watershed transformation. The optic disc to cup ratio (CDR) is calculated which is an important parameter for glaucoma diagnosis. The presented algorithm is evaluated against publically available DRIVE dataset. The presented methodology achieved 88% average sensitivity and 80% average overlap. The average CDR detected is 0.1983.
Segmentation of Optic Disk and Optic Cup from Retinal Image
2015
Retinal image analysis is becoming a prominent tool for detection of eye disease. Glaucoma is one of the serious eye disease. Presently manual inspections of optic disk and cup is a standard procedure which has its own limits for mass screening .In this paper we present an automatic optic disk segmentation and cup segmentation technique for glaucoma detection from retinal images. Detection of glaucoma from retinal image is a difficult task because of occlusions of blood vessel. Optic disk and cup segmentation method which is based on pixel intensities which are considered as a primary evidences of optic disk and cup boundaries. The result shows effectiveness in segmentation of optic disk and cup from retinal image. Keywords— Cup, Neuroretinal Rim, Optic Disk (OD), Retinal Images, Segmentation.
Automatic Segmentation of Optic Disc in Eye Fundus Images: A Survey
Optic disc detection and segmentation is one of the key elements for automatic retinal disease screening systems. The aim of this survey paper is to review, categorize and compare the optic disc detection algorithms and methodologies, giving a description of each of them, highlighting their key points and performance measures. Accordingly, this survey firstly overviews the anatomy of the eye fundus showing its main structural components along with their properties and functions. Consequently, the survey reviews the image enhancement techniques and also categorizes the image segmentation methodologies for the optic disc which include property-based methods, methods based on convergence of blood vessels, and model-based methods. The performance of segmentation algorithms is evaluated using a number of publicly available databases of retinal images via evaluation metrics which include accuracy and true positive rate (i.e. sensitivity). The survey, at the end, describes the different abnormalities occurring within the optic disc region.
AUTOMATIC DETECTION AND SEGMENTATION OF OPTIC DISC IN RETINAL IMAGES
IAEME PUBLICATION, 2019
Reliable and efficient Optic Disc (OD) localization and segmentation are important tasks in automatic eye disease screening. The Optic Disc (OD) center and margin are typically essential landmarks in establishing a frame of reference for classifying retinal and optic nerve pathology. This project presents a fast and automatic Optic Disc localization and segmentation algorithm developed for retinal disease screening. Morphological filtering is used to remove blood vessels and bright regions other than the Optic Disc from a retinal image. Experimentation was performed on 100 images from the publicly available MESSIDOR database. The OD location methodology succeeded in 78 out of 100 images (78% success). Its ef iciency, robustness and accuracy make the OD localization and segmentation scheme described herein suitable for automatic retinal disease screening in a variety of clinical settings. By detecting eye disease early through automated screening algorithms, treatment would become more effective and significant savings in health care costs could be realized.
Automatic detection and segmentation of optic disc and fovea in retinal images
IET Image Processing, 2018
Feature extraction from retinal images is gaining popularity worldwide as many pathologies are proved having connections with these features. Automatic detection of these features makes it easier for the specialist ophthalmologists to analyse them without spending exhaustive time to segment them manually. The proposed method automatically detects the optic disc (OD) using histogram-based template matching combined with the maximum sum of vessel information in the retinal image. The OD region is segmented by using the circular Hough transform. For detecting fovea, the retinal image is uniformly divided into three horizontal strips and the strip including the detected OD is selected. Contrast of the horizontal strip containing the OD region is then enhanced using a series of image processing steps. The macula region is first detected in the OD strip using various morphological operations and connected component analysis. The fovea is located inside this detected macular region. The proposed method achieves an OD detection accuracy over 95% upon testing on seven public databases and on our locally developed database, University of Auckland Diabetic Retinopathy database (UoA-DR). The average OD boundary segmentation overlap score, sensitivity and fovea detection accuracy achieved are 0.86, 0.968 and 97.26% respectively.
Optic Disk Segmentation Using Histogram Analysis
2022
In the field of disease diagnosis with ophthalmic aids, automatic segmentation of the retinal optic disc is required. The main challenge in OD segmentation is to determine the exact location of the OD and remove noise in the retinal image. This paper proposes a method for automatic optical disc segmentation on color retinal fundus images using histogram analysis. Based on the properties of the optical disk, where the optical disk tends to occupy a high intensity. This method has been applied to the Digital Retinal Database for Vessel Extraction (DRIVE)and MESSIDOR database. The experimental results show that the proposed automatic optical segmentation method has an accuracy of 55% for DRIVE dataset and 89% for MESSIDOR database.
Computer Methods and Programs in Biomedicine, 2015
Development of automatic retinal disease diagnosis systems based on retinal image computer analysis can provide remarkably quicker screening programs for early detection. Such systems are mainly focused on the detection of the earliest ophthalmic signs of illness and require previous identification of fundal landmark features such as optic disc (OD), fovea or blood vessels. A methodology for accurate center-position location and OD retinal region segmentation on digital fundus images is presented in this paper. The methodology performs a set of iterative opening-closing morphological operations on the original retinography intensity channel to produce a bright region-enhanced image. Taking blood vessel confluence at the OD into account, a 2-step automatic thresholding procedure is then applied to obtain a reduced region of interest, where the center and the OD pixel region are finally obtained by performing the circular Hough transform on a set of OD boundary candidates generated through the application of the Prewitt edge detector. The methodology was evaluated on 1200 and 1748 fundus images from the publicly available MESSIDOR and MESSIDOR-2 databases, acquired from diabetic patients and thus being clinical cases of interest within the framework of automated diagnosis of retinal diseases associated to diabetes mellitus. This methodology proved highly accurate in OD-center location: average Euclidean distance between the methodology-provided and actual OD-center position was 6.08, 9.22 and 9.72 pixels for retinas of 910, 1380 and 1455 pixels in size, respectively. On the other hand, OD segmentation evaluation was performed in terms of Jaccard and Dice coefficients, as well as the mean average distance between estimated and actual OD boundaries. Comparison with the results reported by other reviewed OD segmentation methodologies shows our proposal renders better overall performance. Its effectiveness and robustness make this proposed automated OD location and segmentation method a suitable tool to be integrated into a complete prescreening system for early diagnosis of retinal diseases.