Detection of Optic Cup Disc using Morphological Pixel Classification Technique. (original) (raw)
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Design and Analysis of Segmentation and Region Growing Method of Diagnosing Eye Images
Diagnosing the abnormal regions in various medical images is one of the critical issues because these images contain different types of random noises and attenuation artifacts. This paper proposes an automatic morphological segmentation and region growing method to change the representation of an image into something that is more meaningful and easier to analyze. There are several methods that intend to perform segmentation, but it is difficult to adapt easily and diagnose accurately. To resolve this problem, this paper aims to present an adaptable automatic morphological segmentation and region growing method that can be applied to any type of medical image which is exactly diagnosed even with the small changes that occur in the image. This proposed method is based in a model of morph function which applies the morphological operator to a gray scale image. Morphological segment technique is used to segment the image and selecting the specific image objects, thinning the object to diagnose the region. After using a morphological operation to expose the basic elements within an image, it is often useful to extract and analyze specific information about those image elements. The region grow function performs region growing for a given region within an N-dimensional array by finding all pixels within the array that are connected to neighboring region pixels and that fall within provided constraints. This technique was applied to segment the medical image, diagnosing the interested object from the grown region in all medical images.
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.
Medical Imaging 2003: Image Processing, 2003
Age-Related Macular Degeneration (ARMD) is the leading cause of irreversible visual loss among the elderly in the US and Europe. A computer-based system has been developed to provide the ability to track the position and margin of the ARMD associated lesion; drusen. Variations in the subject's retinal pigmentation, size and profusion of the lesions, and differences in image illumination and quality present significant challenges to most segmentation algorithms. An algorithm is presented that first classifies the image to optimize the variables of a mathematical morphology algorithm. A binary image is found by applying Otsu's method to the reconstructed image. Lesion size and area distribution statistics are then calculated. For training and validation, the University of Wisconsin provided longitudinal images of 22 subjects from their 10 year Beaver Dam Study. Using the Wisconsin Age-Related Maculopathy Grading System, three graders classified the retinal images according to drusen size and area of involvement. The percentages within the acceptable error between the three graders and the computer are as follows: Grader-A: Area: 84% Size: 81%; Grader-B: Area: 63% Size: 76%; Grader-C: Area: 81% Size: 88%. To validate the segmented position and boundary one grader was asked to digitally outline the drusen boundary. The average accuracy based on sensitivity and specificity was 0.87 for thirty four marked regions.
Segmentation of the optic disk in color eye fundus images using an adaptive morphological approach
Computers in Biology and Medicine, 2010
The identification of some important retinal anatomical regions is a prerequisite for the computer aided diagnosis of several retinal diseases. In this paper, we propose a new adaptive method for the automatic segmentation of the optic disk in digital color fundus images, using mathematical morphology. The proposed method has been designed to be robust under varying illumination and image acquisition conditions, common in eye fundus imaging. Our experimental results based on two publicly available eye fundus image databases are encouraging, and indicate that our approach potentially can achieve a better performance than other known methods proposed in the literature. Using the DRIVE database (which consists of 40 retinal images), our method achieves a success rate of 100% in the correct location of the optic disk, with 41.47% of mean overlap. In the DIARETDB1 database (which consists of 89 retinal images), the optic disk is correctly located in 97.75% of the images, with a mean overlap of 43.65%.
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.
REVIEW ON IMAGE PROCESSING TECHNIQUES FOR OPTIC DISC SEGMENTATION
IJITME, 2018
The review paper describes various image processing techniques for optic cup and disc segmentation for automatic detection of glaucoma. Glaucoma is one among major causes of blindness in working population. Early detection of Glaucoma through automated retinal image analysis helps in preventing vision loss. The end-to-end processing pipeline for Glaucoma detection from retinal images includes the detection of optic disc (OD), neuroretinal rim (NRR), and optic cup (OC) segmentation, feature computation from the segmented OD and OC, and estimation of Glaucoma from these features. Optic disc (OD) segmentation from retinal images is the preliminary step in developing the diagnostic tool for early Glaucoma detection. The segmented OD is preprocessed to highlight the NRR and OC area. A multi-layer perceptron with 12-D feature vector is used for pixel classification based OC segmentation. Cup-to-disc ratio and other contextual features are extracted from the segmented OD and OC. Experimental evaluation shows that the proposed methodology can be reliably utilized in screening programs for early glaucoma detection.
16th Int'l Conf. Computer and Information Technology, 2014
Reliable, fast and efficient optic disc localization and blood-vessel detection are the primary tasks in computer analyses of retinal image. Most of the existing algorithms suffer due to inconsistent image contrast, varying individual condition, noises and computational complexity. This paper presents an algorithm to automatically detect landmark features of retinal image, such as optic disc and blood vessel. First, optic disc and blood vessel pixels are detected from blue plane of the image. Then, using OD location the vessel pixels are connected. The detection scheme utilizes basic operations like edge detection, binary thresholding and morphological operation. This method was evaluated on standard retinal image databases, such as STARE and DRIVE. Experimental results demonstrate that the high accuracy achieved by the proposed method is comparable to that reported by the most accurate methods in literature in terms of accuracy. Thus the method may provide a reliable solution in automatic mass screening and diagnosis of the retinal diseases because of its simplicity and substantial reduction of execution time.
International Journal of Research in Engineering and Technology, 2014
Diabetic retinopathy is the common cause of blindness. This paper presents the mathematical morphology method to detect and eliminate the optic disc (OD) and the blood vessels. Detection of optic disc and the blood vessels are the necessary steps in the detection of diabetic retinopathy because the blood vessels and the optic disc are the normal features of the retinal image. And also, the optic disc and the exudates are the brightest portion of the image. Detection of optic disc and the blood vessels can help the ophthalmologists to detect the diseases earlier and faster. Optic disc and the blood vessels are detected and eliminated by using mathematical morphology methods such as closing, filling, morphological reconstruction and Otsu algorithm. The objective of this paper is to detect the normal features of the image. By using the result, the ophthalmologists can detect the diseases easily.