Structure tensor based automated detection of macular edema and central serous retinopathy using optical coherence tomography images.

Macular edema (ME) and central serous retinopathy (CSR) are two macular diseases that affect the central vision of a person if they are left untreated. Optical coherence tomography (OCT) imaging is the latest eye examination technique that shows a cross-sectional region of the retinal layers and that can be used to detect many retinal disorders in an early stage. Many researchers have done clinical studies on ME and CSR and reported significant findings in macular OCT scans. However, this paper proposes an automated method for the classification of ME and CSR from OCT images using a support vector machine (SVM) classifier. Five distinct features (three based on the thickness profiles of the sub-retinal layers and two based on cyst fluids within the sub-retinal layers) are extracted from 30 labeled images (10 ME, 10 CSR, and 10 healthy), and SVM is trained on these. We applied our proposed algorithm on 90 time-domain OCT (TD-OCT) images (30 ME, 30 CSR, 30 healthy) of 73 patients. Our algorithm correctly classified 88 out of 90 subjects with accuracy, sensitivity, and specificity of 97.77%, 100%, and 93.33%, respectively.

[1]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[3]  Adeel M. Syed,et al.  Automated segmentation of subretinal layers for the detection of macular edema. , 2016, Applied optics.

[4]  P. Ozdal,et al.  Comparison of autofluorescence and optical coherence tomography findings in acute and chronic central serous chorioretinopathy. , 2014, International journal of ophthalmology.

[5]  Sina Farsiu,et al.  Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images. , 2014, Biomedical optics express.

[6]  S. Lee,et al.  Automated characterization of pigment epithelial detachment by optical coherence tomography. , 2012, Investigative ophthalmology & visual science.

[7]  Carmen A Puliafito,et al.  Automated detection of retinal layer structures on optical coherence tomography images. , 2005, Optics express.

[8]  Jason Noble,et al.  Central serous chorioretinopathy: update on pathophysiology and treatment. , 2013, Survey of ophthalmology.

[9]  A Shrestha,et al.  Optical coherence tomographic assessment of macular thickness and morphological patterns in diabetic macular edema: prognosis after modified grid photocoagulation. , 2012, Nepalese journal of ophthalmology : a biannual peer-reviewed academic journal of the Nepal Ophthalmic Society : NEPJOPH.

[10]  R. Klein,et al.  Clinically significant macular edema and survival in type 1 and type 2 diabetes. , 2008, American journal of ophthalmology.

[11]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

[12]  M. Ávila,et al.  Detection of diabetic foveal edema with biomicroscopy, fluorescein angiography and optical coherence tomography. , 2008, Arquivos brasileiros de oftalmologia.

[13]  Junaid Salam Wani,et al.  ROLE OF OPTICAL COHERENCE TOMOGRAPHY IN CENTRAL SEROUS CHORIORETINOPATHY , 2015 .

[14]  Amy L. Oldenburg,et al.  Automated Segmentation of Intraretinal Cystoid Fluid in Optical Coherence Tomography , 2012, IEEE Transactions on Biomedical Engineering.