A Framework for Detecting Glaucomatous Progression in the Optic Nerve Head of an Eye Using Proper Orthogonal Decomposition

Glaucoma is the second leading cause of blindness worldwide. Often, the optic nerve head (ONH) glaucomatous damage and ONH changes occur prior to visual field loss and are observable in vivo. Thus, digital image analysis is a promising choice for detecting the onset and/or progression of glaucoma. In this paper, we present a new framework for detecting glaucomatous changes in the ONH of an eye using the method of proper orthogonal decomposition (POD). A baseline topograph subspace was constructed for each eye to describe the structure of the ONH of the eye at a reference/baseline condition using POD. Any glaucomatous changes in the ONH of the eye present during a follow-up exam were estimated by comparing the follow-up ONH topography with its baseline topograph subspace representation. Image correspondence measures of L 1-norm and L 2-norm, correlation, and image Euclidean distance (IMED) were used to quantify the ONH changes. An ONH topographic library built from the Louisiana State University Experimental Glaucoma study was used to evaluate the performance of the proposed method. The area under the receiver operating characteristic curves (AUCs) was used to compare the diagnostic performance of the POD-induced parameters with the parameters of the topographic change analysis (TCA) method. The IMED and L 2-norm parameters in the POD framework provided the highest AUC of 0.94 at 10deg field of imaging and 0.91 at 15deg field of imaging compared to the TCA parameters with an AUC of 0.86 and 0.88, respectively. The proposed POD framework captures the instrument measurement variability and inherent structure variability and shows promise for improving our ability to detect glaucomatous change over time in glaucoma management.

[1]  A L Boyer,et al.  Investigation of an FFT-based correlation technique for verification of radiation treatment setup. , 1991, Medical physics.

[2]  Augusto Azuara-Blanco,et al.  Handbook of Glaucoma , 2001 .

[3]  S. Kingman Glaucoma is second leading cause of blindness globally. , 2004, Bulletin of the World Health Organization.

[4]  Michael P. Coleman,et al.  Neuronal death: where does the end begin? , 2007, Trends in Neurosciences.

[5]  J. Caprioli,et al.  Detection of structural damage from glaucoma with confocal laser image analysis. , 1996, Investigative ophthalmology & visual science.

[6]  Martin Salm,et al.  Trends in costs of major eye diseases to Medicare , 2006 .

[7]  S. Sitharama Iyengar,et al.  Real-time restoration of white-light confocal microscope optical sections , 2007, J. Electronic Imaging.

[8]  Robert N Weinreb,et al.  Performance of confocal scanning laser tomograph Topographic Change Analysis (TCA) for assessing glaucomatous progression. , 2009, Investigative ophthalmology & visual science.

[9]  David J. Kriegman,et al.  What Is the Set of Images of an Object Under All Possible Illumination Conditions? , 1998, International Journal of Computer Vision.

[10]  C Massimo Moorfields Regression Analysis , 2003 .

[11]  Nick C. Fox,et al.  An automated algorithm for the computation of brain volume change from sequential MRIs using an iterative principal component analysis and its evaluation for the assessment of whole-brain atrophy rates in patients with probable Alzheimer's disease , 2004, NeuroImage.

[12]  Martin Salm,et al.  Trends in cost of major eye diseases to Medicare, 1991 to 2000. , 2006, American journal of ophthalmology.

[13]  Paul H. Artes,et al.  Criteria for Optic Disc Progression With the Topographical Change Analysis of the Heidelberg Retina Tomograph , 2006 .

[14]  David P Crabb,et al.  A new statistical approach for quantifying change in series of retinal and optic nerve head topography images. , 2005, Investigative ophthalmology & visual science.

[15]  Lucila Ohno-Machado,et al.  The use of receiver operating characteristic curves in biomedical informatics , 2005, J. Biomed. Informatics.

[16]  J. P. Lewis,et al.  Fast Template Matching , 2009 .

[17]  B C Chauhan,et al.  Technique for detecting serial topographic changes in the optic disc and peripapillary retina using scanning laser tomography. , 2000, Investigative ophthalmology & visual science.

[18]  H. Quigley,et al.  The number of people with glaucoma worldwide in 2010 and 2020 , 2006, British Journal of Ophthalmology.

[19]  George Wolberg,et al.  Robust image registration using log-polar transform , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[20]  B C Chauhan,et al.  Optic disc and visual field changes in a prospective longitudinal study of patients with glaucoma: comparison of scanning laser tomography with conventional perimetry and optic disc photography. , 2001, Archives of ophthalmology.

[21]  P. Artes,et al.  Automated analysis of heidelberg retina tomograph optic disc images by glaucoma probability score. , 2006, Investigative ophthalmology & visual science.

[22]  P. J. Green,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[23]  Joonki Paik,et al.  Face Recognition Using Optimized 3D Information from Stereo Images , 2005, ICIAR.

[24]  Philipp Birken,et al.  Numerical Linear Algebra , 2011, Encyclopedia of Parallel Computing.

[25]  Robert N Weinreb,et al.  Comparing neural networks and linear discriminant functions for glaucoma detection using confocal scanning laser ophthalmoscopy of the optic disc. , 2002, Investigative ophthalmology & visual science.

[26]  Thomas E. Nichols,et al.  Nonparametric permutation tests for functional neuroimaging: A primer with examples , 2002, Human brain mapping.

[27]  L Sirovich,et al.  Low-dimensional procedure for the characterization of human faces. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[28]  Arne Dür,et al.  On the Optimality of the Discrete Karhunen--Loève Expansion , 1998 .

[29]  J. Doyle,et al.  Cost of patients with primary open-angle glaucoma: a retrospective study of commercial insurance claims data. , 2007, Ophthalmology (Rochester, Minn.).

[30]  Yan Zhang,et al.  On the Euclidean distance of images , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  E. DeLong,et al.  Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. , 1988, Biometrics.

[32]  Lei Gao,et al.  PCA-based approach for video scene change detection on compressed video , 2006 .

[33]  L. Sirovich Turbulence and the dynamics of coherent structures. I. Coherent structures , 1987 .

[34]  Y. Moisan,et al.  Détection des changements dans une série d'images ERS-1 multidates à l'aide de l'analyse en composantes principales , 1999 .

[35]  Yachen Lin,et al.  Geometric Data Analysis: An Empirical Approach to Dimensionality Reduction and the Study of Patterns , 2002, Technometrics.

[36]  J A Hanley,et al.  Sampling variability of nonparametric estimates of the areas under receiver operating characteristic curves: an update. , 1997, Academic radiology.

[37]  B. Chauhan,et al.  Longitudinal changes in the visual field and optic disc in glaucoma , 2005, Progress in Retinal and Eye Research.

[38]  M. Morales i Ballús,et al.  The number of people with glaucoma worldwide in 2010 and 2020 , 2006 .

[39]  Journal of the Optical Society of America , 1950, Nature.

[40]  Robert N Weinreb,et al.  Confocal scanning laser ophthalmoscopy classifiers and stereophotograph evaluation for prediction of visual field abnormalities in glaucoma-suspect eyes. , 2004, Investigative ophthalmology & visual science.

[41]  Philip P. Chen,et al.  A multicenter, retrospective pilot study of resource use and costs associated with severity of disease in glaucoma. , 2006, Archives of ophthalmology.