Layered vasculature segmentation of color conjunctival image based on wavelet transform

Abstract The vasculature segmentation is one of the essential procedures in the conjunctival image analysis in medicine and biometrics. The vascular patterns segmented from the images are affected by the multi-scale, especially the multi-layer feature of the conjunctival vessel. Based on the Monte Carlo simulation and wavelet transform analysis, a layered vasculature segmentation approach for color conjunctival images were developed, so as to extract the conjunctival vessels by layers from image with fine details, which has great potential in biometrics and the gaze tracking.

[1]  Edward J. Delp,et al.  A New Human Identification Method: Sclera Recognition , 2012, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[2]  Christopher G Owen,et al.  Diabetes and the tortuosity of vessels of the bulbar conjunctiva. , 2008, Ophthalmology.

[3]  T. J. Ellis,et al.  A comparison of manual and automated methods of measuring conjunctival vessel widths from photographic and digital images , 2004, Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians.

[4]  Valery V. Tuchin,et al.  Optical properties of human sclera in spectral range 370–2500 nm , 2010 .

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

[6]  David A Boas,et al.  Monte Carlo simulation of photon migration in 3D turbid media accelerated by graphics processing units. , 2009, Optics express.

[7]  Reza Derakhshani,et al.  Computational methods for objective assessment of conjunctival vascularity , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  Eliza Yingzi Du,et al.  An Efficient Parallel Approach for Sclera Vein Recognition , 2014, IEEE Transactions on Information Forensics and Security.

[9]  Arun Ross,et al.  A Texture-Based Neural Network Classifier for Biometric Identification using Ocular Surface Vasculature , 2007, 2007 International Joint Conference on Neural Networks.

[10]  J.-L. Starck,et al.  Astronomical image and signal processing: looking at noise, information and scale , 2001, IEEE Signal Processing Magazine.

[11]  Reza Derakhshani,et al.  Ocular surface vasculature recognition using curvelet transform , 2017, IET Biom..

[12]  A. Madabhushi,et al.  Histopathological Image Analysis: A Review , 2009, IEEE Reviews in Biomedical Engineering.

[13]  James S Wolffsohn,et al.  Changes in Ocular Physiology, Tear Film Characteristics, and Symptomatology With 18 Months Silicone Hydrogel Contact Lens Wear , 2006, Optometry and vision science : official publication of the American Academy of Optometry.

[14]  Yilei Shao,et al.  Human conjunctival microvasculature assessed with a retinal function imager (RFI). , 2013, Microvascular research.

[15]  P. Tower.  The fundus oculi in monozygotic twins; report of six pairs of identical twins. , 1955, A.M.A. archives of ophthalmology.

[16]  B. I. Chazan,et al.  Conjunctival vascular lesions. Classification and clinical significance with special reference to diabetes , 1969, Acta diabetologia latina.

[17]  P. Bankhead,et al.  Fast Retinal Vessel Detection and Measurement Using Wavelets and Edge Location Refinement , 2012, PloS one.

[18]  Miguel Angel Ferrer-Ballester,et al.  Sclera Recognition - A Survey , 2013, 2013 2nd IAPR Asian Conference on Pattern Recognition.

[19]  Kiyoshi Hoshino,et al.  Measurement of rotational eye movement under blue light irradiation by tracking conjunctival blood vessel ends , 2013, Proceedings of the 2013 IEEE/SICE International Symposium on System Integration.

[20]  A. Vogel,et al.  Optical properties of human sclera, and their consequences for transscleral laser applications , 1991, Lasers in surgery and medicine.

[21]  Qianqian Fang,et al.  Mesh-based Monte Carlo method using fast ray-tracing in Plücker coordinates , 2010, Biomedical optics express.

[22]  Thomas M. Deserno,et al.  Automatic conjunctival provocation test combining Hough circle transform and self-calibrated color measurements , 2013, Medical Imaging.

[23]  A. Joussen,et al.  Vascular plasticity – the role of the angiopoietins in modulating ocular angiogenesis , 2001, Graefe's Archive for Clinical and Experimental Ophthalmology.

[24]  Arun Ross,et al.  Enhancement and Registration Schemes for Matching Conjunctival Vasculature , 2009, ICB.

[25]  Qiang Li,et al.  Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans. , 2003, Medical physics.

[26]  M S Patterson,et al.  Why do veins appear blue? A new look at an old question. , 1996, Applied optics.

[27]  Mohamed-Jalal Fadili,et al.  The Undecimated Wavelet Decomposition and its Reconstruction , 2007, IEEE Transactions on Image Processing.

[28]  Bunyarit Uyyanonvara,et al.  Blood vessel segmentation methodologies in retinal images - A survey , 2012, Comput. Methods Programs Biomed..

[29]  Reza Derakhshani,et al.  Application of pyramidal directional filters for biometric identification using conjunctival vasculature patterns , 2013, 2013 IEEE International Conference on Technologies for Homeland Security (HST).

[30]  M. Shahidi,et al.  Quantitative assessment of conjunctival microvascular circulation of the human eye. , 2010, Microvascular research.

[31]  C. W. Oyster.  The human eye: structure and function , 1999, Nature medicine.

[32]  N. Luke Thomas,et al.  A new approach for human identification using the eye , 2010 .

[33]  Mahnaz Shahidi,et al.  Conjunctival microvascular haemodynamics in sickle cell retinopathy , 2015, Acta ophthalmologica.

[34]  David Alonso-Caneiro,et al.  Anterior eye tissue morphology: Scleral and conjunctival thickness in children and young adults , 2016, Scientific Reports.

[35]  E. Papas,et al.  Key factors in the subjective and objective assessment of conjunctival erythema. , 2000, Investigative ophthalmology & visual science.

[36]  W. Zijlstra,et al.  Visible and Near Infrared Absorption Spectra of Human and Animal Haemoglobin : Determination and Application , 2000 .

[37]  A. Welch,et al.  Optical properties of conjunctiva, sclera, and the ciliary body and their consequences for transscleral cyclophotocoagulation. , 1996, Applied optics.

[38]  Arun Ross,et al.  Multispectral scleral patterns for ocular biometric recognition , 2012, Pattern Recognit. Lett..

[39]  Dirk J. Faber,et al.  A literature review and novel theoretical approach on the optical properties of whole blood , 2013, Lasers in Medical Science.

[40]  Arun Ross,et al.  A comparative analysis of wavelets for vascular similarity measurement , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).