A Review on Cornea Imaging and Processing Techniques

Author(s): James Deva Koresh Hezekiah*, Shanty Chacko.

Journal Name: Current Medical Imaging
Formerly: Current Medical Imaging Reviews

Volume 16 , Issue 3 , 2020

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Graphical Abstract:


Background: Measuring cornea thickness is an essential parameter for patients undergoing refractive Laser-Assisted in SItu Keratomileusis (LASIK) surgeries.

Discussion: This paper describes about the various available imaging and non-imaging methods for identifying cornea thickness and explores the most optimal method for measuring it. Along with the thickness measurement, layer segmentation in the cornea is also an essential parameter for diagnosing and treating eye-related disease and problems. The evaluation supports surgical planning and estimation of the corneal health. After surgery, the thickness estimation and layer segmentation are also necessary for identifying the layer surface disorders.

Conclusion: Hence the paper reviews the available image processing techniques for processing the corneal image for thickness measurement and layer segmentation.

Keywords: OCT, edge detection, image segmentation, cornea thickness, cornea segmentation, cornea layers, MATLAB.

Rabsilber TM, Khoramnia R, Auffarth GU. Anterior chamber measurements using Pentacam rotating Scheimpflug camera. J Cataract Refract Surg 2006; 32(3): 456-9.
[http://dx.doi.org/10.1016/j.jcrs.2005.12.103] [PMID: 16631057]
DelMonte DW, Kim T. Anatomy and physiology of the cornea. J Cataract Refract Surg 2011; 37(3): 588-98.
[http://dx.doi.org/10.1016/j.jcrs.2010.12.037] [PMID: 21333881]
Eichel J, Mishra A, Fieguth P, Clausi D, Bizheva K. A novel algorithm for extraction of the layers of the cornea. Canadian conference on computer and robot vision. IEEE: Kelowna, BC, Canada 2009; pp. 313-20.
Mazzotta C, Raiskup F, Baiocchi S, Scarcelli G, Friedman MD, Traversi C. ACXL beyond Keratoconus: Post-LASIK Ectasia, Post-RK Ectasia and pellucid marginal degeneration Management of Early Progressive Cornea Ectasia. Cham: Springer 2017; pp. 169-96.
Paul T, Lim M, Starr CE, Lloyd HO, Coleman DJ, Silverman RH. Central corneal thickness measured by the Orbscan II system, contact ultrasound pachymetry, and the Artemis 2 system. J Cataract Refract Surg 2008; 34(11): 1906-12.
[http://dx.doi.org/10.1016/j.jcrs.2008.07.013] [PMID: 19006737]
Silverman RH. High-resolution ultrasound imaging of the eye-a review. Clin Exp Ophthalmol 2009; 37(1): 54-67.
[http://dx.doi.org/10.1111/j.1442-9071.2008.01892.x] [PMID: 19138310]
Pavlin CJ, Harasiewicz K, Sherar MD, Foster FS. Clinical use of ultrasound biomicroscopy. Ophthalmology 1991; 98(3): 287-95.
[http://dx.doi.org/10.1016/S0161-6420(91)32298-X] [PMID: 2023747]
Tam ES, Rootman DS. Comparison of central corneal thickness measurements by specular microscopy, ultrasound pachymetry, and ultrasound biomicroscopy. J Cataract Refract Surg 2003; 29(6): 1179-84.
[http://dx.doi.org/10.1016/S0886-3350(02)01921-1] [PMID: 12842687]
Urbak SF, Pedersen JK, Thorsen TT. Ultrasound biomicroscopy. II. Intraobserver and interobserver reproducibility of measurements. Acta Ophthalmol Scand 1998; 76(5): 546-9.
[http://dx.doi.org/10.1034/j.1600-0420.1998.760507.x] [PMID: 9826037]
Al-Farhan HM, Al-Otaibi WM. Comparison of central corneal thickness measurements using ultrasound pachymetry, ultrasound biomicroscopy, and the Artemis-2 VHF scanner in normal eyes. Clin Ophthalmol 2012; 6: 1037-43.
[http://dx.doi.org/10.2147/OPTH.S32955] [PMID: 22848145]
Dada T, Sihota R, Gadia R, Aggarwal A, Mandal S, Gupta V. Comparison of anterior segment optical coherence tomography and ultrasound biomicroscopy for assessment of the anterior segment. J Cataract Refract Surg 2007; 33(5): 837-40.
[http://dx.doi.org/10.1016/j.jcrs.2007.01.021] [PMID: 17466858]
Oliveira CM, Ribeiro C, Franco S. Corneal imaging with slit-scanning and Scheimpflug imaging techniques. Clin Exp Optom 2011; 94(1): 33-42.
[http://dx.doi.org/10.1111/j.1444-0938.2010.00509.x] [PMID: 20718786]
Cairns G, McGhee CN. Orbscan computerized topography: attributes, applications, and limitations. J Cataract Refract Surg 2005; 31(1): 205-20.
[http://dx.doi.org/10.1016/j.jcrs.2004.09.047] [PMID: 15721715]
Yaylali V, Kaufman SC, Thompson HW. Corneal thickness measurements with the Orbscan topography system and ultrasonic pachymetry. J Cataract Refract Surg 1997; 23(9): 1345-50.
[http://dx.doi.org/10.1016/S0886-3350(97)80113-7] [PMID: 9423906]
Marsich MW, Bullimore MA. The repeatability of corneal thickness measures. Cornea 2000; 19(6): 792-5.
[http://dx.doi.org/10.1097/00003226-200011000-00007] [PMID: 11095052]
Soma CH, Craig JP, Brahma A, Malik TY, McGhee CNJ. Comparison of cornea thickness measurements using ultrasound and Orbscan slit-scanning topography in normal and post-LASIK eyes. J Cataract Refract Surg 2001; 27(11): 1823-8.
[http://dx.doi.org/10.1016/S0886-3350(01)01089-6] [PMID: 11709257]
Lackner B, Schmidinger G, Pieh S, Funovics MA, Skorpik C. Repeatability and reproducibility of central corneal thickness measurement with Pentacam, Orbscan, and ultrasound. Optom Vis Sci 2005; 82(10): 892-9.
[http://dx.doi.org/10.1097/01.opx.0000180817.46312.0a] [PMID: 16276321]
Fishman GR, Pons ME, Seedor JA, Liebmann JM, Ritch R. Assessment of central corneal thickness using optical coherence tomography. J Cataract Refract Surg 2005; 31(4): 707-11.
[http://dx.doi.org/10.1016/j.jcrs.2004.09.021] [PMID: 15899446]
Fakhry MA, Artola A, Belda JI, Ayala MJ, Alió JL. Comparison of corneal pachymetry using ultrasound and Orbscan II. J Cataract Refract Surg 2002; 28(2): 248-52.
[http://dx.doi.org/10.1016/S0886-3350(01)01277-9] [PMID: 11821205]
Boscia F, La Tegola MG, Alessio G, Sborgia C. Accuracy of Orbscan optical pachymetry in corneas with haze. J Cataract Refract Surg 2002; 28(2): 253-8.
[http://dx.doi.org/10.1016/S0886-3350(01)01162-2] [PMID: 11821206]
Wong ACM, Wong CC, Yuen NSY, Hui SP. Correlational study of central cornea thickness measurements on Hong Kong Chinese using optical coherence tomography, Orbscan and ultrasound pachymetry. Eye (Lond) 2002; 16(6): 715.
[http://dx.doi.org/10.1038/sj.eye.6700211] [PMID: 12439665]
Suzuki S, Oshika T, Oki K, et al. Corneal thickness measurements: scanning-slit corneal topography and noncontact specular microscopy versus ultrasonic pachymetry. J Cataract Refract Surg 2003; 29(7): 1313-8.
[http://dx.doi.org/10.1016/S0886-3350(03)00123-8] [PMID: 12900238]
Li EY, Mohamed S, Leung CK, et al. Agreement among 3 methods to measure cornea thickness: ultrasound pachymetry, Orbscan II, and Visante anterior segment optical coherence tomography. Ophthalmology 2007; 114(10): 1842-7.
[http://dx.doi.org/10.1016/j.ophtha.2007.02.017] [PMID: 17507097]
Randleman JB, Lynn MJ, Perez-Straziota CE, Weissman HM, Kim SW. Comparison of central and peripheral corneal thickness measurements with scanning-slit, scheimpflug and fourier-domain ocular coherence tomography. Br J Ophthalmol 2015; 99(9): 1176-81.
[http://dx.doi.org/10.1136/bjophthalmol-2014-306340] [PMID: 25824260]
Prisant O, Calderon N, Chastang P, Gatinel D, Hoang-Xuan T. Reliability of pachymetric measurements using orbscan after excimer refractive surgery. Ophthalmology 2003; 110(3): 511-5.
[http://dx.doi.org/10.1016/S0161-6420(02)01298-8] [PMID: 12623813]
González-Méijome JM, Cerviño A, Yebra-Pimentel E, Parafita MA. Central and peripheral corneal thickness measurement with Orbscan II and topographical ultrasound pachymetry. J Cataract Refract Surg 2003; 29(1): 125-32.
[http://dx.doi.org/10.1016/S0886-3350(02)01815-1] [PMID: 12551679]
Jain R, Dilraj G, Grewal SP. Repeatability of corneal parameters with Pentacam after laser in situ keratomileusis. Indian J Ophthalmol 2007; 55(5): 341-7.
[http://dx.doi.org/10.4103/0301-4738.33819] [PMID: 17699942]
Gabriele ML, Wollstein G, Ishikawa H, et al. Optical coherence tomography: history, current status, and laboratory work. Invest Ophthalmol Vis Sci 2011; 52(5): 2425-36.
[http://dx.doi.org/10.1167/iovs.10-6312] [PMID: 21493951]
Hurmeric V, Yoo SH, Mutlu FM. Optical coherence tomography in cornea and refractive surgery. Expert Rev Ophthalmol 2012; 7(3): 241-50.
Kuerten D, Plange N, Koch EC, Koutsonas A, Walter P, Fuest M. Central corneal thickness determination in corneal edema using ultrasound pachymetry, a Scheimpflug camera, and anterior segment OCT. Graefes Arch Clin Exp Ophthalmol 2015; 253(7): 1105-9.
[http://dx.doi.org/10.1007/s00417-015-2998-y] [PMID: 25896108]
Antonios R, Fattah MA, Maalouf F, Abiad B, Awwad ST. Central cornea thickness after cross-linking using high-definition optical coherence tomography, ultrasound, and dual Scheimpflug tomography: a comparative study over one year. Am J Ophthalmol 2016; 167: 38-47.
[http://dx.doi.org/10.1016/j.ajo.2016.04.004] [PMID: 27084001]
Graglia F, Mari J-L, Baikoff G, Sequeira J. Contour detection of the cornea from OCT radial images. In: 29th annual international conference of the IEEE engineering in medicine and biology society. IEEE, Lyon, France 2007; pp. 5612-.
Koozekanani D, Boyer K, Roberts C. Retinal thickness measurements from optical coherence tomography using a Markov boundary model. IEEE Trans Med Imaging 2001; 20(9): 900-16.
[http://dx.doi.org/10.1109/42.952728] [PMID: 11585207]
Eichel J, Mishra A, Fieguth P, Clausi D, Bizheva K. A novel algorithm for extraction of the layers of the cornea.Proceedings of the Canadian conference on computer and robot vision. Washington, DC, USA 2009; pp. 313-20.
Mishra A, Wong A, Zhang W, Clausi D, Fieguth P. Improved interactive medical image segmentation using Enhanced Intelligent Scissors (EIS).30th Annual international conference of the IEEE engineering in medicine and biology society. IEEE: Vancouver, BC, Canada 2008; pp. 3083-6.
Mortensen EN, Barrett WA. Intelligent scissors for image composition.Proceedings of the 22nd annual conference on computer graphics and interactive techniques. SIGGRAPH: Washington, DC: USA 1995; pp. 191-8.
Eichel JA, Bizheva KK, Clausi DA, Fieguth PW. Automated 3D reconstruction and segmentation from optical coherence tomography. European conference on computer vision. Springer; Berlin, Heidelberg 2010; pp. 44-57.
Eichel J, Mishra A, Fieguth P, Clausi D, Bizheva K. A novel algorithm for extraction of the layers of the cornea.Proceedings of the 2009 Canadian conference on computer and robot vision. IEEE: Washington, DC: USA 2009; pp. 313-20.
[http://dx.doi.org/10.1109/CRV.2009.22 ]
Shen M, Cui L, Li M, Zhu D, Wang MR, Wang J. Extended scan depth optical coherence tomography for evaluating ocular surface shape. J Biomed Opt 2011; 16(5) 056007
[http://dx.doi.org/10.1117/1.3578461] [PMID: 21639575]
Larocca F, Chiu SJ, McNabb RP, Kuo AN, Izatt JA, Farsiu S. Robust automatic segmentation of corneal layer boundaries in SDOCT images using graph theory and dynamic programming. Biomed Opt Express 2011; 2(6): 1524-38.
[http://dx.doi.org/10.1364/BOE.2.001524] [PMID: 21698016]
Chiu SJ, Li XT, Nicholas P, Toth CA, Izatt JA, Farsiu S. Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation. Opt Express 2010; 18(18): 19413-28.
[http://dx.doi.org/10.1364/OE.18.019413] [PMID: 20940837]
Williams D, Zheng Y, Bao F, Elsheikh A. Automatic segmentation of anterior segment optical coherence tomography images. J Biomed Opt 2013; 18(5): 56003.
[http://dx.doi.org/10.1117/1.JBO.18.5.056003] [PMID: 23640074]
Jahromi K, Mahdi RK, Dehnavi AM, Peyman A, Hajizadeh F, Ommani M. An automatic algorithm for segmentation of the boundaries of cornea layers in optical coherence tomography images using Gaussian mixture model. J Med Signals Sens 2014; 4(3): 181-93.
[PMID: 25298927]
Danesh H, Kafieh R, Rabbani H, Hajizadeh F. Segmentation of choroidal boundary in enhanced depth imaging OCTs using a multiresolution texture based modeling in graph cuts. Comput Math Methods Med 2014; 2014 479268
[http://dx.doi.org/10.1155/2014/479268] [PMID: 24672579]
Williams D, Zheng Y, Bao F, Elsheikh A. Fast segmentation of anterior segment optical coherence tomography images using graph cut. Eye Vis (Lond) 2015; 2(1): 1.
[http://dx.doi.org/10.1186/s40662-015-0011-9] [PMID: 26605357]
Rabbani H, Kafieh R, Kazemian Jahromi M, et al. Obtaining thickness maps of cornea layers using the optimal algorithm for intracornea layer segmentation. Int J Biomed Imaging 2016; 2016 1420230
[http://dx.doi.org/10.1155/2016/1420230] [PMID: 27247559]
Di Y, Li M-Y, Qiao T, Lu N. Edge detection and mathematic fitting for corneal surface with Matlab software. Int J Ophthalmol 2017; 10(3): 336-42.
[PMID: 28393021]
Wang F, Shi F, Zhu W, et al. Automated boundary segmentation and wound analysis for longitudinal cornea OCT images.Medical Imaging 2017: Biomedical applications in molecular, structural, and functional imaging International Society for Optics and Photonics. 2017; p. 1013708.
Zhang T, Elazab A, Wang X, et al. A novel technique for robust and fast segmentation of cornea layer interfaces based on spectral-domain optical coherence tomography imaging. IEEE Access 2017; 5: 10352-63.
Goceri E, Goceri N. Deep learning in medical image analysis: Recent advances and future trends.International conferences computer graphics, visualization, computer vision and image processing. Portugal 2017; pp. 305-10.
Benou A, Veksler R, Friedman A, Raviv TR. De-noising of contrast-enhanced MRI sequences by an ensemble of expert deep neural networks Deep learning and data labeling for medical applications. Cham: Springer 2016; pp. 95-110.
Qi Dou, Hao Chen, Lequan Yu, et al. Mok, Lin Shi, and Pheng-Ann Heng. Automatic detection of cerebral microbleeds from MR images via 3D convolutional neural networks. IEEE Trans Med Imaging 2016; 35(5): 1182-95.
[http://dx.doi.org/10.1109/TMI.2016.2528129] [PMID: 26886975]
Arevalo J, González FA, Ramos-Pollán R, Oliveira JL, Guevara Lopez MA. Representation learning for mammography mass lesion classification with convolutional neural networks. Comput Methods Programs Biomed 2016; 127: 248-57.
[http://dx.doi.org/10.1016/j.cmpb.2015.12.014] [PMID: 2682690]
Poudel RPK, Lamata P, Montana G. Recurrent fully convolutional neural networks for multi-slice MRI cardiac segmentation Machine learning. Cham: Springer 2016; pp. 83-94.
Avendi MR, Kheradvar A, Jafarkhani H. A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI. Med Image Anal 2016; 30: 108-19.
[http://dx.doi.org/10.1016/j.media.2016.01.005] [PMID: 26917105]
Dou Q, Yu L, Chen H, et al. 3D deeply supervised network for automated segmentation of volumetric medical images. Med Image Anal 2017; 41: 40-54.
[http://dx.doi.org/10.1016/j.media.2017.05.001] [PMID: 28526212]
Roth HR, Le Lu AF, Sohn A, Summers RM. Spatial aggregation of holistically-nested networks for automated pancreas segmentation.International conference on medical image computing and computer- assisted intervention. Springer, Cham 2016; pp. 451-9.

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Year: 2020
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DOI: 10.2174/1573405615666181204125406
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