Development of an efficient algorithm for the detection of macular edema from optical coherence tomography images

Development of an efficient algorithm for the detection of macular edema from optical coherence... Purpose Detection of eye diseases and their treatment is a key to reduce blindness, which impacts human daily needs like driving, reading, writing, etc. Several methods based on image processing have been used to monitor the presence of macular diseases. Optical coherence tomography (OCT) imaging is the most efficient technique used to observe eye diseases. This paper proposes an efficient algorithm to automatically classify normal as well as disease-affected (macular edema) retinal OCT images by using segmentation of Inner Limiting Membrane and the Choroid Layer. Methods In the proposed method, preprocessing of the input image is done to improve the quality and reduce the speckle noise. The layer segmentation is done on the gradient image, and graph theory and dynamic programming algorithm is performed. The feature vectors from segmented image are in terms of thickness profile and cyst fluid parameter, and these features are applied to various classifiers. Results The proposed method was tested with the standard dataset collected from the Department of Ophthalmology, Duke University, and achieved a high accuracy rate of 99.4975%, sensitivity of 100%, and specificity of 99% for the SVM classifier. Conclusions An efficient algorithm is proposed for macular edema detection from OCT images using http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Computer Assisted Radiology and Surgery Springer Journals

Development of an efficient algorithm for the detection of macular edema from optical coherence tomography images

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Publisher
Springer Journals
Copyright
Copyright © 2018 by CARS
Subject
Medicine & Public Health; Imaging / Radiology; Surgery; Health Informatics; Computer Imaging, Vision, Pattern Recognition and Graphics; Computer Science, general
ISSN
1861-6410
eISSN
1861-6429
D.O.I.
10.1007/s11548-018-1795-6
Publisher site
See Article on Publisher Site

Abstract

Purpose Detection of eye diseases and their treatment is a key to reduce blindness, which impacts human daily needs like driving, reading, writing, etc. Several methods based on image processing have been used to monitor the presence of macular diseases. Optical coherence tomography (OCT) imaging is the most efficient technique used to observe eye diseases. This paper proposes an efficient algorithm to automatically classify normal as well as disease-affected (macular edema) retinal OCT images by using segmentation of Inner Limiting Membrane and the Choroid Layer. Methods In the proposed method, preprocessing of the input image is done to improve the quality and reduce the speckle noise. The layer segmentation is done on the gradient image, and graph theory and dynamic programming algorithm is performed. The feature vectors from segmented image are in terms of thickness profile and cyst fluid parameter, and these features are applied to various classifiers. Results The proposed method was tested with the standard dataset collected from the Department of Ophthalmology, Duke University, and achieved a high accuracy rate of 99.4975%, sensitivity of 100%, and specificity of 99% for the SVM classifier. Conclusions An efficient algorithm is proposed for macular edema detection from OCT images using

Journal

International Journal of Computer Assisted Radiology and SurgerySpringer Journals

Published: May 29, 2018

References

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