A Framework for Extraction of Inner Limiting Membrane in High Speckle Noisy Images

Optical coherence tomography imaging modality mostly used in biomedical imaging, technique for the examination of retina, and becoming an essential tool for having a 3-D image of retina. Retinal layers analysis of an SD-OCT image facilities the diagnosis and monitoring of various ocular diseases such as Age related Degeneration, Macular Edema, Diabetic Retinopathy and glaucoma. Segmentation of retinal layers in an OCT Images has been a rigorous task due to speckle noise in an image. This paper presents a methods for the extraction of Inner Limiting Membrane (ILM) layer in extremely noisy OCT images. Compared with existing algorithms for the extraction of Inner Limiting Membrane layer (ILM). We also compared diagnostic accuracy of interpolating algorithms and aimed to suggest best interpolating algorithm for delineation of retinal layers focusing glaucoma detection for extremely noisy images. The ILM Layer and Retinal Epithelium Layer will determine the Cup to disc ratio (CDR) that is significant investigating factor for glaucoma diagnosis. The data set having 79 images, including normal, suspect and glaucoma subjects.

[1]  N. Venkateswaran,et al.  Analysis of Segmentation Algorithms in Colour Fundus and OCT Images for Glaucoma Detection , 2015 .

[2]  Paul L. Rosin,et al.  Automated choroidal segmentation of 1060 nm OCT in healthy and pathologic eyes using a statistical model , 2011, Biomedical optics express.

[3]  Ghassan Hamarneh,et al.  Intra-retinal Layer Segmentation in Optical Coherence Tomography Using an Active Contour Approach , 2009, MICCAI.

[4]  Santiago Costantino,et al.  Open-source algorithm for automatic choroid segmentation of OCT volume reconstructions , 2017, Scientific Reports.

[5]  Xiaodong Wu,et al.  Intraretinal Layer Segmentation of Macular Optical Coherence Tomography Images Using Optimal 3-D Graph Search , 2008, IEEE Transactions on Medical Imaging.

[6]  Xiaodong Wu,et al.  Automated 3-D Intraretinal Layer Segmentation of Macular Spectral-Domain Optical Coherence Tomography Images , 2009, IEEE Transactions on Medical Imaging.

[7]  Kim L. Boyer,et al.  Automatic recovery of the optic nervehead geometry in optical coherence tomography , 2006, IEEE Transactions on Medical Imaging.

[8]  Jerry L Prince,et al.  Retinal layer segmentation of macular OCT images using boundary classification , 2013, Biomedical optics express.

[9]  Surinder Singh Pandav,et al.  Evaluation of macular ganglion cell analysis compared to retinal nerve fiber layer thickness for preperimetric glaucoma diagnosis , 2018, Indian journal of ophthalmology.

[10]  Joseph A. Izatt,et al.  Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation , 2010, Optics express.

[11]  Maciej Wojtkowski,et al.  Characterization of outer retinal morphology with high-speed, ultrahigh-resolution optical coherence tomography. , 2008, Investigative ophthalmology & visual science.

[12]  A. Rajan,et al.  Automated Early Detection of Glaucoma in Wavelet Domain Using Optical Coherence Tomography Images , 2015 .

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

[14]  Kim L. Boyer,et al.  Retinal thickness measurements from optical coherence tomography using a Markov boundary model , 2001, IEEE Transactions on Medical Imaging.

[15]  Anum Abdul Salam,et al.  Automated Inner Limiting Membrane Segmentation in OCT Retinal Images for Glaucoma Detection , 2018 .

[16]  S. Shenbaga Devi,et al.  Automatic Detection of Glaucoma Using Optical Coherence Tomography Image , 2012 .

[17]  Muhammad Moazam Fraz,et al.  Computer Vision Techniques Applied for Diagnostic Analysis of Retinal OCT Images: A Review , 2016, Archives of Computational Methods in Engineering.

[18]  M. Usman Akram,et al.  Detection of Glaucoma Using Cup to Disc Ratio From Spectral Domain Optical Coherence Tomography Images , 2018, IEEE Access.

[19]  T R Ganesh Babu,et al.  Optic nerve head segmentation using fundus images and optical coherence tomography images for glaucoma detection. , 2015, Biomedical papers of the Medical Faculty of the University Palacky, Olomouc, Czechoslovakia.

[20]  Maciej Wojtkowski,et al.  Retinal assessment using optical coherence tomography , 2006, Progress in Retinal and Eye Research.

[21]  Boris Hermann,et al.  Robust segmentation of intraretinal layers in the normal human fovea using a novel statistical model based on texture and shape analysis. , 2010, Optics express.

[22]  Alfonso Antón,et al.  Diagnostic Accuracy of Spectralis SD OCT Automated Macular Layers Segmentation to Discriminate Normal from Early Glaucomatous Eyes. , 2017, Ophthalmology.

[23]  F J Muñoz-Negrete,et al.  Cup-to-disc ratio: agreement between slit-lamp indirect ophthalmoscopic estimation and stratus optical coherence tomography measurement , 2007, Eye.

[24]  Wolfgang Drexler,et al.  State-of-the-art retinal optical coherence tomography , 2008, Progress in Retinal and Eye Research.

[25]  F. Medeiros,et al.  The pathophysiology and treatment of glaucoma: a review. , 2014, JAMA.

[26]  Muhammad Usman Akram,et al.  Clinical and technical perspective of glaucoma detection using OCT and fundus images: A review , 2017, 2017 1st International Conference on Next Generation Computing Applications (NextComp).

[27]  Shafin Rahman,et al.  An Approach for Automated Segmentation of Retinal Layers In Peripapillary Spectralis SDOCT Images Using Curve Regularisation , 2017 .

[28]  M. Shahidi,et al.  Quantitative thickness measurement of retinal layers imaged by optical coherence tomography. , 2005, American journal of ophthalmology.

[29]  Eun Ji Lee,et al.  Comparison of the Abilities of SD-OCT and SS-OCT in Evaluating the Thickness of the Macular Inner Retinal Layer for Glaucoma Diagnosis , 2016, PloS one.

[30]  J. Schuman,et al.  Optical coherence tomography. , 2000, Science.