05-04-2016, 12:14 PM
09-04-2016, 12:56 PM
matlab code for retinal image optic disc extraction
Abstract
In this article, we propose a new method for localizing optic disc in retinal images. Localizing the optic disc and its center is the first step of most vessel segmentation, disease diagnostic, and retinal recognition algorithms. We use optic disc of the first four retinal images in DRIVE dataset to extract the histograms of each color component. Then, we calculate the average of histograms for each color as template for localizing the center of optic disc. The DRIVE, STARE, and a local dataset including 273 retinal images are used to evaluate the proposed algorithm. The success rate was 100, 91.36, and 98.9%, respectively.
Introduction
Retina is the innermost layer of the eye which can be visualized using adequate apparatus such as fundus camera. The two main structures used in retinal image analysis are blood vessels and optic disc. Optic disc is the brightest region in the retinal image and the blood vessels originate from its center [1]. Optic disc is a key reference for recognition algorithms [2, 3], blood vessels segmentation [4], and diagnosing some diseases such as diabetes [5]. Histogram is the main character of each image and histogram-based methods are used as the first step of most preprocessing methods to improve the contrast and illumination of retina images. One of the main drawbacks of uneven illumination in retina images and their poor quality is the inability to analyze the optic disc. Applying illumination equalization (histogram equalization, histogram specification, and other normalization methods) as preprocessing methods to retina images considerably improves the contrast, and illumination for further analysis tasks such as optic disc localization and vessel segmentation [6, 7]. In this article, we propose a new method based on the histograms of some optic discs extracted from retinal images. For this purpose, we extract the optic disc of the first four retinal images in DRIVE dataset. Then, we calculate the average of histograms for each color component as template to localize the center of optic disc.
The rest of this article is organized as follows. “Review of previous methods” section is devoted to review the latest proposed methods for optic disc localization. In “Anatomy of the retina” section, we briefly review the anatomy of retina. “Method” section presents the proposed method for optic disc localization. Experimental results are given in “Results” section. Finally, “Conclusion and future work” section is devoted to concluding remarks.
Abstract
In this article, we propose a new method for localizing optic disc in retinal images. Localizing the optic disc and its center is the first step of most vessel segmentation, disease diagnostic, and retinal recognition algorithms. We use optic disc of the first four retinal images in DRIVE dataset to extract the histograms of each color component. Then, we calculate the average of histograms for each color as template for localizing the center of optic disc. The DRIVE, STARE, and a local dataset including 273 retinal images are used to evaluate the proposed algorithm. The success rate was 100, 91.36, and 98.9%, respectively.
Introduction
Retina is the innermost layer of the eye which can be visualized using adequate apparatus such as fundus camera. The two main structures used in retinal image analysis are blood vessels and optic disc. Optic disc is the brightest region in the retinal image and the blood vessels originate from its center [1]. Optic disc is a key reference for recognition algorithms [2, 3], blood vessels segmentation [4], and diagnosing some diseases such as diabetes [5]. Histogram is the main character of each image and histogram-based methods are used as the first step of most preprocessing methods to improve the contrast and illumination of retina images. One of the main drawbacks of uneven illumination in retina images and their poor quality is the inability to analyze the optic disc. Applying illumination equalization (histogram equalization, histogram specification, and other normalization methods) as preprocessing methods to retina images considerably improves the contrast, and illumination for further analysis tasks such as optic disc localization and vessel segmentation [6, 7]. In this article, we propose a new method based on the histograms of some optic discs extracted from retinal images. For this purpose, we extract the optic disc of the first four retinal images in DRIVE dataset. Then, we calculate the average of histograms for each color component as template to localize the center of optic disc.
The rest of this article is organized as follows. “Review of previous methods” section is devoted to review the latest proposed methods for optic disc localization. In “Anatomy of the retina” section, we briefly review the anatomy of retina. “Method” section presents the proposed method for optic disc localization. Experimental results are given in “Results” section. Finally, “Conclusion and future work” section is devoted to concluding remarks.