Automated Optic Nerve Head Detection Based on Different Retinal Vasculature Segmentation Methods and Mathematical Morphology

Computer vision and image processing techniques provide important assistance to physicians and relieve their work load in different tasks. In particular, identifying objects of interest such as lesions and anatomical structures from the image is a challenging and iterative process that can be done by using computer vision and image processing approaches in a successful manner. Optic Nerve Head (ONH) detection is a crucial step in retinal image analysis algorithms. The goal of ONH detection is to find and detect other retinal landmarks and lesions and their corresponding diameters, to use as a length reference to measure objects in the retina. The objective of this study is to apply three retinal vessel segmentation methods, Laplacian-of-Gaussian edge detector, Canny edge detector, and Matched filter edge detector for detection of the ONH either in the normal fundus images or in the presence of retinal lesions (e.g. diabetic retinopathy). The steps for the segmentation are as following: 1) Smoothing: suppress as much noise as possible, without destroying the true edges, 2) Enhancement: apply a filter to enhance the quality of the edges in the image (sharpening), 3) Detection: determine which edge pixels should be discarded as noise and which should be retained by thresholding the edge strength and edge size, 4) Localization: determine the exact location of an edge by edge thinning or linking. To evaluate the accuracy of our proposed method, we compare the output of our proposed method with the ground truth data that collected by ophthalmologists on retinal images belonging to a test set of 120 images. As shown in the results section, by using the Laplacianof-Gaussian vessel segmentation, our automated algorithm finds 18 ONHs in true location for 20 color images in the CHASE-DB database and all images in the DRIVE database. For the Canny vessel segmentation, our automated algorithm finds 16 ONHs in true location for 20 images in the CHASE-DB database and 32 out of 40 images in the DRIVE database. And lastly, using matched filter in the vessel segmentation, our algorithm finds 19 ONHs in true location for 20 images in CHASE-DB database and all images in the DRIVE.

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

[2]  Faraz Kalantari,et al.  Attenuation correction in SPECT images using attenuation map estimation with its emission data , 2017, Medical Imaging.

[3]  T. Williamson,et al.  Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy , 2015, Comput. Medical Imaging Graph..

[4]  Muhammad Moazam Fraz,et al.  Localization and segmentation of optic disc in retinal images using circular Hough transform and grow-cut algorithm , 2016, PeerJ.

[5]  Aliaa A. A. Youssif,et al.  Optic Disc Detection From Normalized Digital Fundus Images by Means of a Vessels' Direction Matched Filter , 2008, IEEE Transactions on Medical Imaging.

[6]  Tamiki Komatsuzaki,et al.  Single Molecule Data Analysis: An Introduction , 2016, 1606.00403.

[7]  Meysam Tavakoli,et al.  Effect of two different preprocessing steps in detection of optic nerve head in fundus images , 2017, Medical Imaging.

[8]  Malay Kishore Dutta,et al.  Automated method for hierarchal detection and grading of diabetic retinopathy , 2018, Comput. methods Biomech. Biomed. Eng. Imaging Vis..

[9]  Nigel H. Lovell,et al.  Automated Segmentation of Optic Disc Boundary and Diameter Calculation Using Fundus Imagery , 2015, CIMSim 2015.

[10]  Langis Gagnon,et al.  Fast and robust optic disc detection using pyramidal decomposition and Hausdorff-based template matching , 2001, IEEE Transactions on Medical Imaging.

[11]  Huiqi Li,et al.  Automated feature extraction in color retinal images by a model based approach , 2004, IEEE Transactions on Biomedical Engineering.

[12]  T. Banaee,et al.  Automated optic nerve head detection in fluorescein angiography fundus images , 2011, 2011 IEEE Nuclear Science Symposium Conference Record.

[13]  M H Goldbaum,et al.  Interpretation of automated perimetry for glaucoma by neural network. , 1994, Investigative ophthalmology & visual science.

[14]  Nasser Kehtarnavaz,et al.  Computationally efficient optic nerve head detection in retinal fundus images , 2014, Biomed. Signal Process. Control..

[15]  Ieee Xplore,et al.  IEEE Transactions on Pattern Analysis and Machine Intelligence Information for Authors , 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  T. Banaee,et al.  Radon transform technique for linear structures detection: Application to vessel detection in fluorescein angiography fundus images , 2011, 2011 IEEE Nuclear Science Symposium Conference Record.

[17]  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.

[18]  Majid Mirmehdi,et al.  Comparison of colour spaces for optic disc localisation in retinal images , 2002, Object recognition supported by user interaction for service robots.

[19]  Bunyarit Uyyanonvara,et al.  Vessel transform for automatic optic disk detection in retinal images , 2015, IET Image Process..

[20]  Woo Jin Kim,et al.  Proceedings of Medical Image Understanding and Analysis , 2015 .

[21]  O. Chang,et al.  Neural networks for optic nerve detection in digital optic fundus images , 2016, 2016 IEEE International Conference on Automatica (ICA-ACCA).

[22]  A. Ruggeri,et al.  Quantitative description of vessel features in hypertensive retinopathy fundus images , 2001 .

[23]  Muhammad Moazam Fraz,et al.  Delineation of blood vessels in pediatric retinal images using decision trees-based ensemble classification , 2014, International Journal of Computer Assisted Radiology and Surgery.

[24]  Agus Harjoko,et al.  Optic disc and cup segmentation by automatic thresholding with morphological operation for glaucoma evaluation , 2017, Signal Image Video Process..

[25]  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.

[26]  Jamshid Dehmeshki,et al.  Automated detection of proliferative diabetic retinopathy using a modified line operator and dual classification , 2014, Comput. Methods Programs Biomed..

[27]  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.

[28]  Heinrich Niemann,et al.  Automated segmentation of the optic nerve head for diagnosis of glaucoma , 2005, Medical Image Anal..

[29]  Langis Gagnon,et al.  Procedure to detect anatomical structures in optical fundus images , 2001, SPIE Medical Imaging.

[30]  Bahreyni Tousi Seyed Mohammad Hossein,et al.  AUTOMATIC DETECTION OF MICROANEURYSMS IN COLOR FUNDUS IMAGES USING A LOCAL RADON TRANSFORM METHOD , 2009 .

[31]  Erkki Oja,et al.  Probabilistic and non-probabilistic Hough transforms: overview and comparisons , 1995, Image Vis. Comput..

[32]  Andrew Hunter,et al.  Measurement of retinal vessel widths from fundus images based on 2-D modeling , 2004, IEEE Transactions on Medical Imaging.

[33]  M. Goldbaum,et al.  Detection of blood vessels in retinal images using two-dimensional matched filters. , 1989, IEEE transactions on medical imaging.

[34]  Frédéric Zana,et al.  Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation , 2001, IEEE Trans. Image Process..

[35]  Mahnaz Shahidi,et al.  Method for quantitative assessment of retinal vessel tortuosity in optical coherence tomography angiography applied to sickle cell retinopathy. , 2017, Biomedical optics express.

[36]  Mahnaz Shahidi,et al.  Automated Assessment of Hemodynamics in the Conjunctival Microvasculature Network , 2015, IEEE Transactions on Medical Imaging.

[37]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[38]  Bunyarit Uyyanonvara,et al.  An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation , 2012, IEEE Transactions on Biomedical Engineering.

[39]  Mahnaz Shahidi,et al.  Automated fine structure image analysis method for discrimination of diabetic retinopathy stage using conjunctival microvasculature images. , 2016, Biomedical optics express.

[40]  Shinichi Tamura,et al.  Zero-crossing interval correction in tracing eye-fundus blood vessels , 1988, Pattern Recognit..

[41]  Enrico Grisan,et al.  Detection of optic disc in retinal images by means of a geometrical model of vessel structure , 2004, IEEE Transactions on Medical Imaging.

[42]  Mariano Alcañiz Raya,et al.  Automatic Detection of Optic Disc Based on PCA and Mathematical Morphology , 2013, IEEE Transactions on Medical Imaging.

[43]  Thomas Walter,et al.  Segmentation of Color Fundus Images of the Human Retina: Detection of the Optic Disc and the Vascular Tree Using Morphological Techniques , 2001, ISMDA.

[44]  R. A. Abdel-Ghafar,et al.  Detection and Characterisation of the Optic Disk in Glaucoma and Diabetic Retinopathy , 2004 .

[45]  Paolo Remagnino,et al.  Quantification of blood vessel calibre in retinal images of multi-ethnic school children using a model based approach , 2013, Comput. Medical Imaging Graph..

[46]  Reza Pourreza-Shahri,et al.  A complementary method for automated detection of microaneurysms in fluorescein angiography fundus images to assess diabetic retinopathy , 2013, Pattern Recognit..