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
International Journal of Computer Assisted Radiology and Surgery – Springer Journals
Published: May 29, 2018
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