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Abstract
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Article Information:
Segmentation and Classification of Optic Disc in Retinal Images
S. Vasanthi and R.S.D. Wahida Banu
Corresponding Author: S. Vasanthi
Submitted: June 11, 2014
Accepted: July 19, 2014
Published: October 05, 2014 |
Abstract:
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Image segmentation plays a vital role in image analysis for diagnosis of various retinopathy diseases. For the detection of glaucoma and diabetic retinopathy, manual examination of the optic disc is the standard clinical procedure. The proposed method makes use of the circular transform to automatically locate and extract the Optic Disc (OD) from the retinal fundus images. The circular transform operates with radial line operator which uses the multiple radial line segments on every pixel of the image. The maximum variation pixels along each radial line segments are taken to detect and segment OD. The input retinal images are preprocessed before applying circular transform. The optic disc diameter and the distance from optic disc to macula are found for a sample of 20 images. An Extreme Learning Machine classifier is used to train the neural network to classify the images as normal or abnormal. Its performance is compared with Support Vector Machine in terms of computation time and accuracy. It is found that computation time is less than 0.1 sec and accuracy is 97.14% for Extreme Learning Machine classifier.
Key words: Circular transform, extreme learning machine, macula, optic disc, segmentation, ,
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Abstract
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Cite this Reference:
S. Vasanthi and R.S.D. Wahida Banu, . Segmentation and Classification of Optic Disc in Retinal Images. Research Journal of Applied Sciences, Engineering and Technology, (13): 1563-1571.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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