Spectral retinal image processing and analysis for ophthalmology

Lauri Laaksonen Spectral retinal image processing and analysis for ophthalmology Lappeenranta, 2016 159 p. Acta Universitatis Lappeenrantaensis 699 Diss. Lappeenranta University of Technology ISBN 978-952-265-956-9 ISBN 978-952-265-957-6 (PDF) ISSN-L 1456-4491 ISSN 1456-4491 Diabetic retinopathy, age-related macular degeneration and glaucoma are the leading causes of blindness worldwide. Automatic methods for diagnosis exist, but their performance is limited by the quality of the data. Spectral retinal images provide a significantly better representation of the colour information than common grayscale or red-green-blue retinal imaging, having the potential to improve the performance of automatic diagnosis methods. This work studies the image processing techniques required for composing spectral retinal images with accurate reflection spectra, including wavelength channel image registration, spectral and spatial calibration, illumination correction, and the estimation of depth information from image disparities. The composition of a spectral retinal image database of patients with diabetic retinopathy is described. The database includes gold standards for a number of pathologies and retinal structures, marked by two expert ophthalmologists. The diagnostic applications of the reflectance spectra are studied using supervised classifiers for lesion detection. In addition, inversion of a model of light transport is used to estimate histological parameters from the reflectance spectra. Experimental results suggest that the methods for composing, calibrating and postprocessing spectral images presented in this work can be used to improve the quality of the spectral data. The experiments on the direct and indirect use of the data show the diagnostic potential of spectral retinal data over standard retinal images. The use of spectral data could improve automatic and semi-automated diagnostics for the screening of retinal diseases, for the quantitative detection of retinal changes for follow-up, clinically relevant end-points for clinical studies and development of new therapeutic modalities.

[1]  I. Deary,et al.  Retinal image analysis: Concepts, applications and potential , 2006, Progress in Retinal and Eye Research.

[2]  Hidekata Hontani,et al.  3D fundus pattern reconstruction and display from multiple fundus images , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[3]  Nicholas Ayache,et al.  Symmetric Log-Domain Diffeomorphic Registration: A Demons-Based Approach , 2008, MICCAI.

[4]  Vincent Lepetit,et al.  Accurate and Efficient Linear Structure Segmentation by Leveraging Ad Hoc Features with Learned Filters , 2012, MICCAI.

[5]  Elisa Ricci,et al.  Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification , 2007, IEEE Transactions on Medical Imaging.

[6]  E. Claridge,et al.  Multispectral imaging of the ocular fundus using light emitting diode illumination. , 2010, The Review of scientific instruments.

[7]  Emily W. Gower,et al.  Economic impact of visual impairment and blindness in the United States. , 2007, Archives of ophthalmology.

[8]  Che-Hao Chang,et al.  Improved Hand Tracking System , 2012, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  Lawrence P. L. Iu,et al.  The inner segment/outer segment junction: what have we learnt so far? , 2012, Current opinion in ophthalmology.

[10]  Sushma G. Thorat.  Locating the Optic Nerve in a Retinal Image Using the Fuzzy Convergence of the Blood Vessels , 2014 .

[11]  W. Hunold,et al.  Spectrophotometric Determination of the Melanin Pigmentation of the Human Ocular Fundus in vivo , 1974 .

[12]  Christiane,et al.  World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. , 2004, Journal international de bioethique = International journal of bioethics.

[13]  Jussi Tuunanen,et al.  Modelling of changes in electricity end-use and their impacts on electricity distribution , 2015 .

[14]  Robert W. G. Hunt,et al.  The reproduction of colour , 1957 .

[15]  Jean-Yves Bouguet,et al.  Camera calibration toolbox for matlab , 2001 .

[16]  Asoke K. Nandi,et al.  Automated localisation of optic disk and fovea in retinal fundus images , 2008, 2008 16th European Signal Processing Conference.

[17]  Kenneth R. Alexander,et al.  Human macular pigment assessed by imaging fundus reflectometry , 1989, Vision Research.

[18]  Almasi S. Maguya.  Use of airborne laser scanner data in demanding forest conditions , 2015 .

[19]  C. Paterson,et al.  Measuring retinal vessel tortuosity in 10-year-old children: validation of the Computer-Assisted Image Analysis of the Retina (CAIAR) program. , 2009, Investigative ophthalmology & visual science.

[20]  Jukka Rantamäki.  Utilization of Statistical Methods for Management in the Forest Industry , 2016 .

[21]  R Hiller,et al.  Blindness caused by diabetic retinopathy. , 1974, American journal of ophthalmology.

[22]  Chris A. Johnson,et al.  A Comparison of Noninvasive Objective and Subjective Measurements of the Optical Density of Human Ocular Media , 2001, Optometry and vision science : official publication of the American Academy of Optometry.

[23]  Tomi Kauppi,et al.  Eye Fundus Image Analysis for Automatic Detection of Diabetic Retinopathy , 2010 .

[24]  Chia-Ling Tsai,et al.  The Edge-Driven Dual-Bootstrap Iterative Closest Point Algorithm for Registration of Multimodal Fluorescein Angiogram Sequence , 2010, IEEE Transactions on Medical Imaging.

[25]  Hidekata Hontani,et al.  3D Fundus Shape Reconstruction and Display From Stereo Fundus Images , 2000, MVA.

[26]  K. Chan,et al.  Towards automatic detection of age-related macular degeneration in retinal fundus images , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[27]  Christos Davatzikos,et al.  GRAM: A framework for geodesic registration on anatomical manifolds , 2010, Medical Image Anal..

[28]  K. Murashko.  Thermal modelling of commercial lithium-ion batteries , 2016 .

[29]  Markku Hauta-Kasari,et al.  Extending Diabetic Retinopathy Imaging from Color to Spectra , 2009, SCIA.

[30]  Janne Heikkilä,et al.  A four-step camera calibration procedure with implicit image correction , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[31]  Guoliang Fan,et al.  3-D Retinal Curvature Estimation , 2009, IEEE Transactions on Information Technology in Biomedicine.

[32]  A. Alm,et al.  Ocular and optic nerve blood flow at normal and increased intraocular pressures in monkeys (Macaca irus): a study with radioactively labelled microspheres including flow determinations in brain and some other tissues. , 1973, Experimental eye research.

[33]  C. Sinthanayothin,et al.  Automated detection of diabetic retinopathy on digital fundus images , 2002, Diabetic medicine : a journal of the British Diabetic Association.

[34]  Felipe Orihuela-Espina,et al.  Quantitative analysis of multi-spectral fundus images , 2006, Medical Image Anal..

[35]  E Claridge,et al.  Monte Carlo modelling of the spectral reflectance of the human eye. , 2002, Physics in medicine and biology.

[36]  Joni-Kristian Kämäräinen,et al.  The DIARETDB1 Diabetic Retinopathy Database and Evaluation Protocol , 2007, BMVC.

[37]  Sing Bing Kang,et al.  Can We Calibrate a Camera Using an Image of a Flat, Textureless Lambertian Surface? , 2000, ECCV.

[38]  P E Stanga,et al.  High-resolution hyperspectral imaging of the retina with a modified fundus camera. , 2010, Journal francais d'ophtalmologie.

[39]  Zhengyou Zhang,et al.  Flexible camera calibration by viewing a plane from unknown orientations , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[40]  P. D. de Jong,et al.  Macular pigment and melanin in age-related maculopathy in a general population. , 2002, Investigative ophthalmology & visual science.

[41]  Mong-Li Lee,et al.  An effective approach to detect lesions in color retinal images , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[42]  Daniel Rueckert,et al.  Groupwise Combined Segmentation and Registration for Atlas Construction , 2007, MICCAI.

[43]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[44]  Majid Mirmehdi,et al.  Automatic Recognition of Exudative Maculopathy using Fuzzy C- Means Clustering and Neural Networks , 2001 .

[45]  Jean-Philippe Thirion,et al.  Image matching as a diffusion process: an analogy with Maxwell's demons , 1998, Medical Image Anal..

[46]  Edward A. Boettner,et al.  Transmission of the Ocular Media , 1962 .

[47]  J. Beach,et al.  Oximetry of retinal vessels by dual-wavelength imaging: calibration and influence of pigmentation. , 1999, Journal of applied physiology.

[48]  Olga Veksler,et al.  Fast approximate energy minimization via graph cuts , 2001, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[49]  László G. Nyúl,et al.  Classifying Glaucoma with Image-Based Features from Fundus Photographs , 2007, DAGM-Symposium.

[50]  Francisco Fumero,et al.  RIM-ONE: An open retinal image database for optic nerve evaluation , 2011, 2011 24th International Symposium on Computer-Based Medical Systems (CBMS).

[51]  Roberto Hornero,et al.  Assessment of four neural network based classifiers to automatically detect red lesions in retinal images. , 2010, Medical engineering & physics.

[52]  Susan Schneider,et al.  Ranibizumab versus verteporfin for neovascular age-related macular degeneration. , 2006, The New England journal of medicine.

[53]  N. D. Wangsa-Wirawan,et al.  Retinal Oxygen Fundamental and Clinical Aspects , 2003 .

[54]  Attila Budai,et al.  A Public Database for the Evaluation of Fundus Image Segmentation Algorithms , 2011 .

[55]  Dinesh Kumar,et al.  Validating retinal fundus image analysis algorithms: issues and a proposal. , 2013, Investigative ophthalmology & visual science.

[56]  D. Hill,et al.  Non-rigid image registration: theory and practice. , 2004, The British journal of radiology.

[57]  D. Schweitzer,et al.  In vivo measurement of the oxygen saturation of retinal vessels in healthy volunteers , 1999, IEEE Transactions on Biomedical Engineering.

[58]  Pierre Vandergheynst,et al.  FREAK: Fast Retina Keypoint , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[59]  Markku Hauta-Kasari,et al.  Spectral Imaging of the Human Retina and Computationally Determined Optimal Illuminants for Diabetic Retinopathy Lesion Detection , 2011 .

[60]  Mona Kathryn Garvin,et al.  3D reconstruction of the optic nerve head using stereo fundus images for computer-aided diagnosis of glaucoma , 2010, Medical Imaging.

[61]  Irene Barbazetto,et al.  A Method of Drusen Measurement Based on the Geometry of Fundus Reflectance , 2003, Biomedical engineering online.

[62]  Lasse Lensu,et al.  Refining Coarse Manual Segmentations with Stable Probability Regions , 2015 .

[63]  João Manuel R S Tavares,et al.  Medical image registration: a review , 2014, Computer methods in biomechanics and biomedical engineering.

[64]  T. Williamson,et al.  Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. , 1996, The British journal of ophthalmology.

[65]  Erno Vanhala.  The role of business model in computer game development organizations , 2015 .

[66]  Its'hak Dinstein,et al.  New maximum likelihood motion estimation schemes for noisy ultrasound images , 2002, Pattern Recognit..

[67]  Max A. Viergever,et al.  Ridge-based vessel segmentation in color images of the retina , 2004, IEEE Transactions on Medical Imaging.

[68]  Yulong Shen,et al.  Registration and fusion of retinal images-an evaluation study , 2003, IEEE Transactions on Medical Imaging.

[69]  Ayyakkannu Manivannan,et al.  Automated drusen detection in retinal images using analytical modelling algorithms , 2011, Biomedical engineering online.

[70]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[71]  Roberto Hornero,et al.  A novel automatic image processing algorithm for detection of hard exudates based on retinal image analysis. , 2008, Medical engineering & physics.

[72]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[73]  Emil Kurvinen.  Design and Simulation of High-Speed Rotating Electrical Machinery , 2016 .

[74]  S S Hayreh,et al.  Segmental nature of the choroidal vasculature. , 1975, The British journal of ophthalmology.

[75]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[76]  Treatment techniques and clinical guidelines for photocoagulation of diabetic macular edema. Early Treatment Diabetic Retinopathy Study Report Number 2. Early Treatment Diabetic Retinopathy Study Research Group. , 1987, Ophthalmology.

[77]  Anne Strauss.  Handbook Of Medical Image Processing And Analysis , 2016 .

[78]  J. Boyce,et al.  Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening , 2004, Diabetic medicine : a journal of the British Diabetic Association.

[79]  Anna-Maria Talonpoika,et al.  Financial working capital - management and measurement , 2016 .

[80]  Andreas Birk,et al.  Maximum likelihood mapping with spectral image registration , 2010, 2010 IEEE International Conference on Robotics and Automation.

[81]  Pasi Nuutinen.  Power Electronic Converters in Low-Voltage Direct Current Distribution – Analysis and Implementation , 2015 .

[82]  T. Teng,et al.  Progress towards automated diabetic ocular screening: A review of image analysis and intelligent systems for diabetic retinopathy , 2006, Medical and Biological Engineering and Computing.

[83]  T. Berendschot,et al.  Simultaneous measurement of foveal spectral reflectance and cone-photoreceptor directionality. , 2002, Applied optics.

[84]  Enrico Grisan,et al.  Luminosity and contrast normalization in retinal images , 2005, Medical Image Anal..

[85]  Xing Zhang,et al.  Salient Feature Region: A New Method for Retinal Image Registration , 2011, IEEE Transactions on Information Technology in Biomedicine.

[86]  J. Olson,et al.  Automated detection of microaneurysms in digital red‐free photographs: a diabetic retinopathy screening tool , 2000, Diabetic medicine : a journal of the British Diabetic Association.

[87]  F. Meer.  The effectiveness of spectral similarity measures for the analysis of hyperspectral imagery , 2006 .

[88]  James M. Beach,et al.  Multispectral fundus imaging for early detection of diabetic retinopathy , 1999, Photonics West - Biomedical Optics.

[89]  Liang Gao,et al.  Snapshot hyperspectral retinal camera with the Image Mapping Spectrometer (IMS) , 2011, Biomedical optics express.

[90]  B. van Ginneken,et al.  Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis. , 2007, Investigative ophthalmology & visual science.

[91]  Charles V. Stewart,et al.  Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy , 2006, IEEE Transactions on Biomedical Engineering.

[92]  Luc Van Gool,et al.  A stratified approach to metric self-calibration , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[93]  P. Burlina,et al.  Automated classification of severity of age-related macular degeneration from fundus photographs. , 2013, Investigative ophthalmology & visual science.

[94]  Elsi Strand,et al.  Enhancement of ultrafiltration process by pretreatment in recovery of hemicelluloses from wood extracts , 2016 .

[95]  Juho Salminen.  The role of collective intelligence in crowdsourcing innovation , 2015 .

[96]  Nikolaos G. Bourbakis,et al.  A survey of skin-color modeling and detection methods , 2007, Pattern Recognit..

[97]  R. Chipman,et al.  Diffuse spectral fundus reflectance measured using subretinally placed spectralon. , 2007, Journal of biomedical optics.

[98]  Mohamad Ezral Baharudin.  Real-time simulation of multibody systems with applications for working mobile vehicles , 2016 .

[99]  Roberto Hornero,et al.  Neural network based detection of hard exudates in retinal images , 2009, Comput. Methods Programs Biomed..

[100]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[101]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[102]  Robert W. G. Hunt,et al.  The Reproduction of Colour: Sixth Edition , 2004 .

[103]  Curtis L. Meinert,et al.  Photocoagulation treatment of proliferative diabetic retinopathy: the second report of diabetic retinopathy study findings. , 1978, Ophthalmology.

[104]  K. Herold.  Impact of Word-of-Mouth on consumer decision-making: An information processing perspective in the context of a high-involvement service , 2015 .

[105]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[106]  Joni-Kristian Kämäräinen,et al.  Generative part-based Gabor object detector , 2015, Pattern Recognit. Lett..

[107]  F. Delori,et al.  Spectral reflectance of the human ocular fundus. , 1989, Applied optics.

[108]  Bram van Ginneken,et al.  Fast detection of the optic disc and fovea in color fundus photographs , 2009, Medical Image Anal..

[109]  Stephen T. C. Wong,et al.  oint registration and segmentation of serial lung CT images for image-guided ung cancer diagnosis and therapy , 2009 .

[110]  U. Rajendra Acharya,et al.  Algorithms for the Automated Detection of Diabetic Retinopathy Using Digital Fundus Images: A Review , 2012, Journal of Medical Systems.

[111]  J. Alió,et al.  The Aging of the Human Lens , 2008 .

[112]  Pascale Massin,et al.  A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina , 2002, IEEE Transactions on Medical Imaging.

[113]  Dietrich Schweitzer,et al.  Quantitative reflection spectroscopy at the human ocular fundus. , 2002, Physics in medicine and biology.

[114]  L Wang,et al.  MCML--Monte Carlo modeling of light transport in multi-layered tissues. , 1995, Computer methods and programs in biomedicine.

[115]  Harold S. Stone,et al.  Blind cross-spectral image registration using prefiltering and Fourier-based translation detection , 2002, IEEE Trans. Geosci. Remote. Sens..

[116]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[117]  Jon Atli Benediktsson,et al.  Automatic retinal oximetry. , 2006, Investigative ophthalmology & visual science.

[118]  P. Scanlon,et al.  Article Commentary: The English national screening programme for sight-threatening diabetic retinopathy , 2008, Journal of medical screening.

[119]  E M Kohner,et al.  Role of Blood Flow and Impaired Autoregulation in the Pathogenesis of Diabetic Retinopathy , 1995, Diabetes.

[120]  Juha Peippo,et al.  A modified nominal stress method for fatigue assessment of steel plates with thermally cut edges , 2015 .

[121]  Daniel W. Wilson,et al.  Snapshot hyperspectral imaging in ophthalmology. , 2007, Journal of biomedical optics.

[122]  C. Sinthanayothin,et al.  Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images , 1999, The British journal of ophthalmology.

[123]  D. Van Norren,et al.  Spectral reflectance of the human eye , 1986, Vision Research.

[124]  Elli Angelopoulou,et al.  Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database , 2013, IET Image Process..

[125]  Ville Leminen.  Leak-proof Heat Sealing of Press-Formed Paperboard Trays , 2016 .

[126]  Nicholas Ayache,et al.  Non-parametric Diffeomorphic Image Registration with the Demons Algorithm , 2007, MICCAI.

[127]  F. Fitzke,et al.  Refractive index of the human corneal epithelium and stroma. , 1995, Journal of refractive surgery.

[128]  M. Sonka,et al.  Retinal Imaging and Image Analysis. , 2010, IEEE transactions on medical imaging.

[129]  Xiaochen Yang.  Development of a New Welding Product Quality Control and Management System Model for China , 2016 .

[130]  Anurag Mittal,et al.  Automated feature extraction for early detection of diabetic retinopathy in fundus images , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[131]  Bram van Ginneken,et al.  Segmentation of the Optic Disc, Macula and Vascular Arch in Fundus Photographs , 2007, IEEE Transactions on Medical Imaging.

[132]  Kenneth W. Tobin,et al.  Exudate-based diabetic macular edema detection in fundus images using publicly available datasets , 2012, Medical Image Anal..

[133]  Rui Bernardes,et al.  Digital Ocular Fundus Imaging: A Review , 2011, Ophthalmologica.

[134]  Tien Yin Wong,et al.  ORIGA-light: An online retinal fundus image database for glaucoma analysis and research , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[135]  Pierre Soille,et al.  Morphological Image Analysis: Principles and Applications , 2003 .

[136]  Petri Valtonen,et al.  Distributed energy resources in an electricity retailer’s short-term profit optimization , 2015 .

[137]  L. Damico,et al.  DEVELOPMENT OF RANIBIZUMAB, AN ANTI–VASCULAR ENDOTHELIAL GROWTH FACTOR ANTIGEN BINDING FRAGMENT, AS THERAPY FOR NEOVASCULAR AGE-RELATED MACULAR DEGENERATION , 2006, Retina.

[138]  J C Javitt,et al.  Cost effectiveness of current approaches to the control of retinopathy in type I diabetics. , 1989, Ophthalmology.

[139]  U. Rajendra Acharya,et al.  Automated Diagnosis of Glaucoma Using Texture and Higher Order Spectra Features , 2011, IEEE Transactions on Information Technology in Biomedicine.

[140]  Roy Taylor.  Handbook of Retinal Screening in Diabetes , 2006 .

[141]  D. Norren,et al.  The Pathways of Light Measured in Fundus Reflectometry , 1996, Vision Research.

[142]  Panu Tanninen.  Press forming of paperboard – advancement of converting tools and process control , 2015 .

[143]  Gérard G. Medioni,et al.  Retinal image registration from 2D to 3D , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[144]  Alun D. Hughes,et al.  3D Reconstruction of the Retinal Arterial Tree Using Subject-Specific Fundus Images , 2009 .

[145]  Joseph M. Reinhardt,et al.  Feature-based pairwise retinal image registration by radial distortion correction , 2007, SPIE Medical Imaging.

[146]  J. Beach,et al.  Hyperspectral imaging for measurement of oxygen saturation in the optic nerve head. , 2004, Investigative ophthalmology & visual science.

[147]  Guido Gerig,et al.  Unbiased diffeomorphic atlas construction for computational anatomy , 2004, NeuroImage.

[148]  Andrew R. Harvey,et al.  Spectral imaging in a snapshot , 2000, SPIE BiOS.

[149]  Roland Siegwart,et al.  BRISK: Binary Robust invariant scalable keypoints , 2011, 2011 International Conference on Computer Vision.

[150]  Brandon J Lujan,et al.  Calibration of fundus images using spectral domain optical coherence tomography. , 2008, Ophthalmic surgery, lasers & imaging : the official journal of the International Society for Imaging in the Eye.

[151]  G Muyo,et al.  Spectral imaging of the retina , 2011, Eye.

[152]  David Maberley,et al.  Screening for diabetic retinopathy in James Bay, Ontario: a cost-effectiveness analysis. , 2003, CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne.

[153]  Gang Yao,et al.  Monte Carlo model for studying the effects of melanin concentrations on retina light absorption. , 2008, Journal of the Optical Society of America. A, Optics, image science, and vision.

[154]  Yulia Panova,et al.  Public-private partnership investments in dry ports – Russian logistics markets and risks , 2016 .

[155]  Bram van Ginneken,et al.  Active Learning for an Efficient Training Strategy of Computer-Aided Diagnosis Systems: Application to Diabetic Retinopathy Screening , 2010, MICCAI.

[156]  Andrew R Harvey,et al.  Assessment of acute mild hypoxia on retinal oxygen saturation using snapshot retinal oximetry. , 2013, Investigative ophthalmology & visual science.

[157]  Jirí Jan,et al.  Retrospective Illumination Correction of Retinal Images , 2010, Int. J. Biomed. Imaging.

[158]  Mark R. Pickering,et al.  Multi-spectral remote sensing image registration via spatial relationship analysis on sift keypoints , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[159]  Donald D Duncan,et al.  Measurement of oxygen saturation in the retina with a spectroscopic sensitive multi aperture camera. , 2008, Optics express.

[160]  Päivi Porras.  Utilising student profiles in mathematics course arrangements , 2015 .

[161]  S. Drance,et al.  Color Doppler imaging and spectral analysis of the optic nerve vasculature in glaucoma. , 1995, American journal of ophthalmology.

[162]  Asoke K. Nandi,et al.  Detection of exudates in retinal images using a pure splitting technique , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[163]  Olli Väntsi.  Utilization of recycled mineral wool as filler in wood plastic composites , 2015 .

[164]  Samuel G. Jacobson,et al.  Specular Reflection From The Surface Of The Retina , 1989, Photonics West - Lasers and Applications in Science and Engineering.

[165]  P F Sharp,et al.  The value of digital imaging in diabetic retinopathy. , 2003, Health technology assessment.

[166]  Qin Li,et al.  Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs , 2010, IEEE Transactions on Medical Imaging.

[167]  Bostjan Likar,et al.  A review of 3D/2D registration methods for image-guided interventions , 2012, Medical Image Anal..

[168]  Jirí Jan,et al.  Illumination correction and contrast equalization in colour fundus images , 2011, 2011 19th European Signal Processing Conference.

[169]  Oskari Halminen.  Multibody models for examination of touchdown bearing systems , 2016 .

[170]  Vladimir Vezhnevets,et al.  “GrowCut” - Interactive Multi-Label N-D Image Segmentation By Cellular Automata , 2005 .

[171]  B. Horecker.  THE ABSORPTION SPECTRA OF HEMOGLOBIN AND ITS DERIVATIVES IN THE VISIBLE AND NEAR INFRA-RED REGIONS , 1943 .

[172]  Markku Hauta-Kasari,et al.  Multichannel Spectral Image Enhancement for Visualizing Diabetic Retinopathy Lesions , 2014, ICISP.

[173]  Saila Rosas.  Co-operative acquisitions – the contextual factors and challenges for co-operatives when acquiring an investorowned firm , 2015 .

[174]  T. Berendschot,et al.  Objective determination of the macular pigment optical density using fundus reflectance spectroscopy. , 2004, Archives of biochemistry and biophysics.

[176]  Dinggang Shen,et al.  ABSORB: Atlas building by Self-Organized Registration and Bundling , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[177]  Katriina Mielonen,et al.  The effect of cationic-anionic polyelectrolyte multilayer surface treatment on inkjet ink spreading and print quality , 2015 .

[178]  Jennifer H. Acton,et al.  Recovery of macular pigment spectrum in vivo using hyperspectral image analysis. , 2011, Journal of biomedical optics.

[179]  Arturo Espinosa-Romero,et al.  3D Reconstruction of Retinal Blood Vessels from Two Views , 2004, ICVGIP.

[180]  Maged Habib,et al.  REVIEW - A reference data set for retinal vessel profiles , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[181]  O. D. Faugeras,et al.  Camera Self-Calibration: Theory and Experiments , 1992, ECCV.

[182]  Jean-Michel Morel,et al.  A non-local algorithm for image denoising , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[183]  Dinggang Shen,et al.  Feature‐based groupwise registration by hierarchical anatomical correspondence detection , 2012, Human brain mapping.

[184]  T. Mihashi,et al.  Validity of retinal oxygen saturation analysis: Hyperspectral imaging in visible wavelength with fundus camera and liquid crystal wavelength tunable filter , 2007 .

[185]  D. D. Smet.  Innovation Ecosystem Perspectives on Financial Services Innovation , 2015 .

[186]  U. Rajendra Acharya,et al.  Application of Higher Order Spectra for the Identification of Diabetes Retinopathy Stages , 2008, Journal of Medical Systems.

[187]  J. Xu,et al.  Comparative study of two calibration methods on fundus camera , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[188]  B. Thomas,et al.  Automated identification of diabetic retinal exudates in digital colour images , 2003, The British journal of ophthalmology.

[189]  A Roggan,et al.  Optical properties of ocular fundus tissues--an in vitro study using the double-integrating-sphere technique and inverse Monte Carlo simulation. , 1995, Physics in medicine and biology.

[190]  F. Delori.  Spectrophotometer for noninvasive measurement of intrinsic fluorescence and reflectance of the ocular fundus. , 1994, Applied Optics.

[191]  Andriy Myronenko,et al.  Maximum Likelihood Motion Estimation in 3D Echocardiography through Non-rigid Registration in Spherical Coordinates , 2009, FIMH.

[192]  Kenneth W. Tobin,et al.  Textureless Macula Swelling Detection With Multiple Retinal Fundus Images , 2011, IEEE Transactions on Biomedical Engineering.

[193]  Matti Pietikäinen,et al.  Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features , 2009, SCIA.

[194]  Markku Hauta-Kasari,et al.  Comparison of image registration methods for composing spectral retinal images , 2014 .

[195]  M. M. Fraza,et al.  Blood vessel segmentation methodologies in retinal images – A survey , 2015 .

[196]  Johanna Choremis,et al.  Use of telemedicine in screening for diabetic retinopathy. , 2003, Canadian journal of ophthalmology. Journal canadien d'ophtalmologie.

[197]  R. Klein,et al.  Causes and prevalence of visual impairment among adults in the United States. , 2004, Archives of ophthalmology.

[198]  Chia-Ling Tsai,et al.  The dual-bootstrap iterative closest point algorithm with application to retinal image registration , 2003, IEEE Transactions on Medical Imaging.

[199]  A.D. Hoover,et al.  Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response , 2000, IEEE Transactions on Medical Imaging.

[200]  Roberto Marcondes Cesar Junior,et al.  Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification , 2005, IEEE Transactions on Medical Imaging.

[201]  Bram van Ginneken,et al.  Comparative study of retinal vessel segmentation methods on a new publicly available database , 2004, SPIE Medical Imaging.

[202]  U. Schmidt-Erfurth,et al.  Comparison of macular pigment in patients with age‐related macular degeneration and healthy control subjects – a study using spectral fundus reflectance , 2012, Acta ophthalmologica.

[203]  Ullrich Köthe,et al.  Ilastik: Interactive learning and segmentation toolkit , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[204]  H. K. Abhyankar,et al.  Image Registration Techniques: An overview , 2009 .

[205]  U. Rajendra Acharya,et al.  An Integrated Index for the Identification of Diabetic Retinopathy Stages Using Texture Parameters , 2012, Journal of Medical Systems.

[206]  Andriy Myronenko,et al.  Intensity-Based Image Registration by Minimizing Residual Complexity , 2010, IEEE Transactions on Medical Imaging.

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

[208]  Linda G. Shapiro,et al.  A SIFT descriptor with global context , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[209]  Tos T. J. M Berendschot,et al.  Fundus reflectance—historical and present ideas , 2003, Progress in Retinal and Eye Research.

[210]  Daniel Rueckert,et al.  Similarity Metrics for Groupwise Non-rigid Registration , 2007, MICCAI.

[211]  Valery V. Tuchin,et al.  Estimation of wavelength dependence of refractive index of collagen fibers of scleral tissue , 2000, European Conference on Biomedical Optics.

[212]  Chia-Ling Tsai,et al.  Registration of Challenging Image Pairs: Initialization, Estimation, and Decision , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[213]  G. Coscas,et al.  A new approach of geodesic reconstruction for drusen segmentation in eye fundus images , 2001, IEEE Transactions on Medical Imaging.

[214]  F. Delori.  Noninvasive technique for oximetry of blood in retinal vessels. , 1988, Applied optics.

[215]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[216]  Pekka Torvinen,et al.  Catching up with competitiveness in emerging markets – An analysis of the role of the firm’s technology management strategies , 2016 .

[217]  Xiaohui Zhang,et al.  Detection and classification of bright lesions in color fundus images , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[218]  A. Ruggeri,et al.  3-D Retinal Surface Inference: Stereo or Monocular Fundus Camera? , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[219]  Jacob Scharcanski,et al.  Segmentation of the optic disk in color eye fundus images using an adaptive morphological approach , 2010, Comput. Biol. Medicine.

[220]  Hong Yan,et al.  A Novel Vessel Segmentation Algorithm for Pathological Retina Images Based on the Divergence of Vector Fields , 2008, IEEE Transactions on Medical Imaging.

[221]  Soili Martikainen.  Development and Effect Analysis of the Asteri Consultative Auditing Process - Safety and Security Management in Educational Institutions , 2016 .

[222]  Gonzalo Muyo,et al.  Light path-length distributions within the retina , 2014, Journal of biomedical optics.

[223]  Ahmed Wasif Reza,et al.  Automatic Tracing of Optic Disc and Exudates from Color Fundus Images Using Fixed and Variable Thresholds , 2009, Journal of Medical Systems.

[224]  S A Burns,et al.  Fundus reflectance and the measurement of crystalline lens density. , 1996, Journal of the Optical Society of America. A, Optics, image science, and vision.

[225]  J. van de Kraats,et al.  Retinal densitometer with the size of a fundus camera. , 1989, Vision research.

[226]  A. Kianto,et al.  Intellectual capital, knowledge management practices and firm performance , 2017 .

[227]  David J. Kriegman,et al.  Globally Optimal Algorithms for Stratified Autocalibration , 2010, International Journal of Computer Vision.

[228]  Srinivas R Sadda,et al.  Retinal imaging in the twenty-first century: state of the art and future directions. , 2014, Ophthalmology.

[229]  Paul A. Viola,et al.  Alignment by Maximization of Mutual Information , 1997, International Journal of Computer Vision.

[230]  K. Noronha,et al.  A review of fundus image analysis for the automated detection of diabetic retinopathy , 2012 .

[231]  Giri Babu Kande,et al.  Segmentation of Exudates and Optic Disk in Retinal Images , 2008, 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing.

[232]  Philip J Rosenfeld,et al.  Optical coherence tomography findings after an intravitreal injection of bevacizumab (avastin) for neovascular age-related macular degeneration. , 2005, Ophthalmic surgery, lasers & imaging : the official journal of the International Society for Imaging in the Eye.

[233]  G. Muyo,et al.  Validation of human whole blood oximetry, using a hyperspectral fundus camera with a model eye. , 2011, Investigative ophthalmology & visual science.

[234]  Abiodun Musa Aibinu,et al.  A new method of correcting uneven illumination problem in fundus images , 2007 .

[235]  Shuqian Luo,et al.  Support vector machine based method for identifying hard exudates in retinal images , 2009, 2009 IEEE Youth Conference on Information, Computing and Telecommunication.

[236]  R. Anderson,et al.  The optics of human skin. , 1981, The Journal of investigative dermatology.

[237]  Andrea Fusiello,et al.  Globally convergent autocalibration using interval analysis , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[238]  V.R.S Mani,et al.  Survey of Medical Image Registration , 2013 .

[239]  Guang Jiang,et al.  Mean shift tracking with graph cuts based image segmentation , 2012, 2012 5th International Congress on Image and Signal Processing.

[240]  K. A. Narayanankutty,et al.  3d reconstruction of human retina from fundus image–a survey , 2012 .

[241]  Timothy Q. Duong,et al.  Magnetic resonance imaging of the retina: A brief historical and future perspective. , 2011, Saudi journal of ophthalmology : official journal of the Saudi Ophthalmological Society.

[242]  I B Styles,et al.  Multispectral retinal image analysis: a novel non-invasive tool for retinal imaging , 2011, Eye.

[243]  Yin Aye Moe,et al.  Automatic Exudate Detection with a Naive Bayes Classifier , 2008 .

[244]  Timothy F. Cootes,et al.  A Unified Information-Theoretic Approach to Groupwise Non-rigid Registration and Model Building , 2005, IPMI.

[245]  V. Turjanmaa,et al.  Determination of retinal blood vessel diameters and arteriovenous ratios in systemic hypertension: comparison of different calculation formulae , 2006, Graefe's Archive for Clinical and Experimental Ophthalmology.

[246]  Ulrich Bartsch,et al.  Calibration-free measurement of the oxygen saturation in human retinal vessels , 1995, Photonics West.

[247]  Richard A. Bone,et al.  Macular pigment, photopigments, and melanin: Distributions in young subjects determined by four-wavelength reflectometry , 2007, Vision Research.

[248]  Tommi Kärkkäinen.  Observations of Acoustic Emission in Power Semiconductors , 2015 .

[249]  OsarehAlireza,et al.  A computational-intelligence-based approach for detection of exudates in diabetic retinopathy images , 2009 .

[250]  Joshua Emuejevoke Omajene.  Underwater Remote Welding Technology for Offshore Structures , 2015 .

[251]  Michalis E. Zervakis,et al.  Detection and segmentation of drusen deposits on human retina: Potential in the diagnosis of age-related macular degeneration , 2003, Medical Image Anal..

[252]  Enrico Grisan,et al.  Model-based illumination correction in retinal images , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[253]  Dietrich Schweitzer,et al.  Optical properties of ocular fundus tissues determined by optical coherence tomography , 2000 .

[254]  U. Rajendra Acharya,et al.  Automated Diagnosis of Glaucoma Using Digital Fundus Images , 2009, Journal of Medical Systems.

[255]  R. Knighton,et al.  Quantitative reflectometry of the ocular fundus , 1995 .

[256]  Manuel Emilio Gegúndez-Arias,et al.  Detecting the Optic Disc Boundary in Digital Fundus Images Using Morphological, Edge Detection, and Feature Extraction Techniques , 2010, IEEE Transactions on Medical Imaging.

[257]  Bram van Ginneken,et al.  Automatic detection of red lesions in digital color fundus photographs , 2005, IEEE Transactions on Medical Imaging.

[258]  S. Resnikoff,et al.  Global data on visual impairment in the year 2002. , 2004, Bulletin of the World Health Organization.

[259]  A. D. Vaiopoulos.  Developing Matlab scripts for image analysis and quality assessment , 2011, Remote Sensing.

[260]  Balraj Naren,et al.  Medical Image Registration , 2022 .

[261]  Joni-Kristian Kämäräinen,et al.  Constructing Benchmark Databases and Protocols for Medical Image Analysis: Diabetic Retinopathy , 2013, Comput. Math. Methods Medicine.

[262]  Devi Vijayan,et al.  Detection of Exudates in Diabetic Retinopathy , 2018, 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[263]  Nikos Paragios,et al.  Deformable Medical Image Registration: A Survey , 2013, IEEE Transactions on Medical Imaging.

[264]  Heidi Forsström-Tuominen.  Collectiveness within startup teams – Leading the way to initiating and managing collective pursuit of opportunities in organizational contexts , 2015 .