Retinal blood vessel extraction employing effective image features and combination of supervised and unsupervised machine learning methods

In medicine, retinal vessel analysis of fundus images is a prominent task for the screening and diagnosis of various ophthalmological and cardiovascular diseases. In this research, a method is proposed for extracting the retinal blood vessels employing a set of effective image features and combination of supervised and unsupervised machine learning techniques. Further to the common features used in extracting blood vessels, three strong features having a significant influence on the accuracy of the vessel extraction are utilized. The selected combination of the different types of individually efficient features results in a rich local information with better discrimination for vessel and non-vessel pixels. The proposed method first extracts the thick and clear vessels in an unsupervised manner, and then, it extracts the thin vessels in a supervised way. The goal of the combination of the supervised and unsupervised methods is to deal with the problem of intra-class high variance of image features calculated from various vessel pixels. The proposed method is evaluated on three publicly available databases DRIVE, STARE and CHASE_DB1. The obtained results (DRIVE: Acc = 0.9531, AUC = 0.9752; STARE: Acc = 0.9691, AUC = 0.9853; CHASE_DB1: Acc = 0.9623, AUC = 0.9789) demonstrate the better performance of the proposed method compared to the state-of-the-art methods.

[1]  José Manuel Bravo,et al.  A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features , 2011, IEEE Transactions on Medical Imaging.

[2]  Paul Y. S. Cheung,et al.  Vessel Extraction Under Non-Uniform Illumination: A Level Set Approach , 2008, IEEE Transactions on Biomedical Engineering.

[3]  J. Bezdek,et al.  FCM: The fuzzy c-means clustering algorithm , 1984 .

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

[5]  Luiz Carlos Rodrigues,et al.  Segmentation of optic disc and blood vessels in retinal images using wavelets, mathematical morphology and Hessian-based multi-scale filtering , 2017, Biomed. Signal Process. Control..

[6]  P. Remagnino,et al.  Retinal image analysis aimed at extraction of vascular structure using linear discriminant classifier , 2013, 2013 International Conference on Computer Medical Applications (ICCMA).

[7]  George Azzopardi,et al.  Trainable COSFIRE filters for vessel delineation with application to retinal images , 2015, Medical Image Anal..

[8]  Juan Humberto Sossa Azuela,et al.  Retinal vessel extraction using Lattice Neural Networks with dendritic processing , 2015, Comput. Biol. Medicine.

[9]  D. Sagi,et al.  Gabor filters as texture discriminator , 1989, Biological Cybernetics.

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

[11]  Bunyarit Uyyanonvara,et al.  A supervised method for retinal blood vessel segmentation using line strength, multiscale Gabor and morphological features , 2011, 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA).

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

[13]  Emanuele Trucco,et al.  Leveraging Multiscale Hessian-Based Enhancement With a Novel Exudate Inpainting Technique for Retinal Vessel Segmentation , 2016, IEEE Journal of Biomedical and Health Informatics.

[14]  Yongtian Wang,et al.  Saliency driven vasculature segmentation with infinite perimeter active contour model , 2017, Neurocomputing.

[15]  Alessandro Bria,et al.  Retinal Vessel Segmentation Through Denoising and Mathematical Morphology , 2017, ICIAP.

[16]  Lei Zhang,et al.  Retinal vessel extraction by matched filter with first-order derivative of Gaussian , 2010, Comput. Biol. Medicine.

[17]  Lei Zhang,et al.  Multi-level deep supervised networks for retinal vessel segmentation , 2017, International Journal of Computer Assisted Radiology and Surgery.

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

[19]  Lucas J. van Vliet,et al.  A nonlinear laplace operator as edge detector in noisy images , 1989, Comput. Vis. Graph. Image Process..

[20]  Bunyarit Uyyanonvara,et al.  An approach to localize the retinal blood vessels using bit planes and centerline detection , 2012, Comput. Methods Programs Biomed..

[21]  Qinmu Peng,et al.  Segmentation of retinal blood vessels using the radial projection and semi-supervised approach , 2011, Pattern Recognit..

[22]  John D. Austin,et al.  Adaptive histogram equalization and its variations , 1987 .

[23]  Yuan Zhang,et al.  Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function , 2018, Neurocomputing.

[24]  Kotagiri Ramamohanarao,et al.  Blood Vessel Segmentation from Color Retinal Images using Unsupervised Texture Classification , 2007, 2007 IEEE International Conference on Image Processing.

[25]  R. GeethaRamani,et al.  Retinal blood vessel segmentation employing image processing and data mining techniques for computerized retinal image analysis , 2016 .

[26]  S. Edward Rajan,et al.  Computerized screening of diabetic retinopathy employing blood vessel segmentation in retinal images , 2014 .

[27]  Yanli Hou,et al.  Automatic Segmentation of Retinal Blood Vessels Based on Improved Multiscale Line Detection , 2014, J. Comput. Sci. Eng..

[28]  David Zhang,et al.  A New Approach to Automated Retinal Vessel Segmentation Using Multiscale Analysis , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

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

[30]  Santi P. Maity,et al.  Retinal blood vessel segmentation using matched filter and Laplacian of Gaussian , 2016, 2016 International Conference on Signal Processing and Communications (SPCOM).

[31]  I. Jolliffe.  Principal Component Analysis and Factor Analysis , 1986 .

[32]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[33]  Antonio Moreno,et al.  Learning ensemble classifiers for diabetic retinopathy assessment , 2017, Artif. Intell. Medicine.

[34]  Tianfu Wang,et al.  A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images , 2016, IEEE Transactions on Medical Imaging.

[35]  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.

[36]  Rajeev Srivastava,et al.  Retinal blood vessels segmentation by using Gumbel probability distribution function based matched filter , 2016, Comput. Methods Programs Biomed..

[37]  Paul Geladi,et al.  Principal Component Analysis , 1987, Comprehensive Chemometrics.

[38]  Gongping Yang,et al.  Hierarchical retinal blood vessel segmentation based on feature and ensemble learning , 2015, Neurocomputing.

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

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

[41]  Francis K. H. Quek,et al.  Vessel extraction in medical images by wave-propagation and traceback , 2001, IEEE Transactions on Medical Imaging.

[42]  Andrew Hunter,et al.  An Active Contour Model for Segmenting and Measuring Retinal Vessels , 2009, IEEE Transactions on Medical Imaging.

[43]  G. Quellec,et al.  Automated analysis of retinal images for detection of referable diabetic retinopathy. , 2013, JAMA ophthalmology.

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

[45]  Ahad Harati,et al.  Vessel segmentation and microaneurysm detection using discriminative dictionary learning and sparse representation , 2017, Comput. Methods Programs Biomed..

[46]  Josef Kittler,et al.  Minimum error thresholding , 1986, Pattern Recognit..

[47]  Ivan Cruz-Aceves,et al.  Blood vessel segmentation in retinal fundus images using Gabor filters, fractional derivatives, and Expectation Maximization , 2018, Appl. Math. Comput..

[48]  Jinkai Cui,et al.  Retinal vessel segmentation in colour fundus images using Extreme Learning Machine , 2017, Comput. Medical Imaging Graph..

[49]  Francis K. H. Quek,et al.  A review of vessel extraction techniques and algorithms , 2004, CSUR.

[50]  Nashwa El-Bendary,et al.  Retinal Blood Vessel Segmentation Approach Based on Mathematical Morphology , 2015 .

[51]  Alan Wee-Chung Liew,et al.  General Retinal Vessel Segmentation Using Regularization-Based Multiconcavity Modeling , 2010, IEEE Transactions on Medical Imaging.

[52]  Rached Tourki,et al.  Computational analysis of blood flow in the retinal arteries and veins using fundus image , 2015, Comput. Math. Appl..

[53]  Federico Bizzarri,et al.  A modular supervised algorithm for vessel segmentation in red-free retinal images , 2008, Comput. Biol. Medicine.

[54]  M. Usman Akram,et al.  Multilayered thresholding-based blood vessel segmentation for screening of diabetic retinopathy , 2011, Engineering with Computers.

[55]  Yongsheng Gao,et al.  Retinal Vessel Segmentation Using Matched Filter with Joint Relative Entropy , 2017, CAIP.

[56]  A. Osareh,et al.  Vessel Segmentation in Retinal Images Using Multi-scale Line Operator and K-Means Clustering , 2014, Journal of medical signals and sensors.

[57]  Shahab Aslani,et al.  A new supervised retinal vessel segmentation method based on robust hybrid features , 2016, Biomed. Signal Process. Control..

[58]  Max A. Viergever,et al.  Multiscale vessel tracking , 2004, IEEE Transactions on Medical Imaging.

[59]  Kotagiri Ramamohanarao,et al.  Automatic Detection of Vascular Bifurcations and Crossovers from Color Retinal Fundus Images , 2007, 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System.

[60]  Chandan Chakraborty,et al.  Small retinal vessels extraction towards proliferative diabetic retinopathy screening , 2012, Expert Syst. Appl..

[61]  João L. Monteiro,et al.  Using a multi-agent system approach for microaneurysm detection in fundus images , 2014, Artif. Intell. Medicine.

[62]  Ali M. Reza,et al.  Realization of the Contrast Limited Adaptive Histogram Equalization (CLAHE) for Real-Time Image Enhancement , 2004, J. VLSI Signal Process..

[63]  Gabriella Sanniti di Baja,et al.  Severe: Segmenting vessels in retina images , 2016, Pattern Recognit. Lett..

[64]  Ali Mahlooji Far,et al.  Retinal Image Analysis Using Curvelet Transform and Multistructure Elements Morphology by Reconstruction , 2011, IEEE Transactions on Biomedical Engineering.

[65]  M. Usman Akram,et al.  Decision support system for detection of hypertensive retinopathy using arteriovenous ratio , 2018, Artif. Intell. Medicine.

[66]  Ana Maria Mendonça,et al.  Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction , 2006, IEEE Transactions on Medical Imaging.