A novel approach for automatic blood vessel extraction in retinal images: complex ripplet-I transform and complex valued artificial neural network

This study determined the features of line, curve, and ridge structures in images using complex ripplet-I and enabled extraction of blood vessel networks from retinal images through a complex valued articial neural network using those features. Forty color fundus images in the DRIVE database and 20 color fundus images in the STARE database were used to test the success of the proposed system. In this study, a complex version of ripplet-I transform was used for therst time. By presenting the directed image for the determination of the unique geometrical properties of the vessel regions, complex ripplet-I transforms showing better performance than other types of multiresolution analysis were combined with a complex valued ANN. The results in the study were reobtained using leave-one-out cross- validation method with bagging technique in order to ensure the stability and correctness of the performance. In the DRIVE database, the highest average accuracy of the system was found to be 98.44% for complex ripplet-I transform and complex valued ANN. For the STARE database (labeled by Adam Hoover), highest average accuracy rates were obtained as 99.25% for complex ripplet-I transforms and complex valued ANN. Similarly, for the other labeled data (by Valentina Kouznetsova), highest average accuracy rates were obtained as 98.03% for complex ripplet-I transforms and complex valued ANN.

[1]  M. Silvia,et al.  Retinal Image Analysis using Ripplet-I Transform and Segmentation using Morphological Gradient , 2012 .

[2]  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.

[3]  Mong-Li Lee,et al.  Segmentation of Retinal Vessels Using Nonlinear Projections , 2007, 2007 IEEE International Conference on Image Processing.

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

[5]  Lili Xu,et al.  A novel method for blood vessel detection from retinal images , 2010, Biomedical engineering online.

[6]  Ajoy Kumar Ray,et al.  Detection of retinal vessels in fundus images through transfer learning of tissue specific photon interaction statistical physics , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

[7]  Wei Jin,et al.  Enhancing retinal image by the Contourlet transform , 2007, Pattern Recognit. Lett..

[8]  Aboul Ella Hassanien,et al.  An improved ant colony system for retinal blood vessel segmentation , 2013, 2013 Federated Conference on Computer Science and Information Systems.

[9]  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.

[10]  Lei Yang,et al.  Ripplet: A new transform for image processing , 2010, J. Vis. Commun. Image Represent..

[11]  Bunyarit Uyyanonvara,et al.  Automatic extraction of retinal vessels based on gradient orientation analysis , 2011, 2011 Eighth International Joint Conference on Computer Science and Software Engineering (JCSSE).

[12]  G.B. Kande,et al.  Retinal vessel segmentation using spatially weighted fuzzy c-means clustering and histogram matching , 2008, 2008 Annual IEEE India Conference.

[13]  P. Remagnino,et al.  Retinal vasculature segmentation by morphological curvature, reconstruction and adapted hysteresis thresholding , 2011, 2011 7th International Conference on Emerging Technologies.

[15]  David L. Donoho,et al.  Digital curvelet transform: strategy, implementation, and experiments , 2000, SPIE Defense + Commercial Sensing.

[16]  Jeffrey E. Boyd,et al.  Automated diagnosis and image understanding with object extraction, object classification, and inferencing in retinal images , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[17]  V. Shanmugam,et al.  Retinal blood vessel segmentation using an Extreme Learning Machine approach , 2013, 2013 IEEE Point-of-Care Healthcare Technologies (PHT).

[18]  Anil A. Bharath,et al.  Segmentation of blood vessels from red-free and fluorescein retinal images , 2007, Medical Image Anal..

[19]  Hideki Kuga,et al.  A computer method of understanding ocular fundus images , 1982, Pattern Recognit..

[20]  reza kharghanian,et al.  Retinal Blood Vessel Segmentation Using Gabor Wavelet and Line Operator , 2012 .

[21]  George D. C. Cavalcanti,et al.  Retinal vessel segmentation using Average of Synthetic Exact Filters and Hessian matrix , 2012, 2012 19th IEEE International Conference on Image Processing.

[22]  Fei Yang,et al.  Retinal vessel extraction by combining radial symmetry transform and iterated graph cuts , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[23]  Qinmu Peng,et al.  A Fast Approach to Retinal Vessel Segmentation , 2010, 2010 Chinese Conference on Pattern Recognition (CCPR).

[24]  Laurent Demanet,et al.  Fast Discrete Curvelet Transforms , 2006, Multiscale Model. Simul..

[25]  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.

[26]  Qinmu Peng,et al.  Retinal Blood Vessels Segmentation Using the Radial Projection and Supervised Classification , 2010, 2010 20th International Conference on Pattern Recognition.

[27]  Ajith V. Pillai,et al.  Comparison of curvelet and contourlet transforms for retinal analysis , 2012, 2012 Annual IEEE India Conference (INDICON).

[28]  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).

[29]  Emmanuel J. Candès,et al.  New multiscale transforms, minimum total variation synthesis: applications to edge-preserving image reconstruction , 2002, Signal Process..

[30]  Isam Hameed,et al.  A novel vessel segmentation algorithm in color images of the retina , 2013, 2013 IEEE Long Island Systems, Applications and Technology Conference (LISAT).

[31]  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).

[33]  Saleh Shahbeig.  Retinal image analysis using multidirectional functors based on geodesic conversions , 2014 .

[34]  M. Usman Akram,et al.  Retinal images: Blood vessel segmentation by threshold probing , 2010, 2010 IEEE Symposium on Industrial Electronics and Applications (ISIEA).

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

[36]  Murat Ceylan,et al.  Blood vessel extraction from retinal images using Complex Wavelet Transform and Complex-Valued Artificial Neural Network , 2013, 2013 36th International Conference on Telecommunications and Signal Processing (TSP).

[37]  Salah Bourennane,et al.  An automatic tracking method for retinal vascular tree extraction , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

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

[39]  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.

[40]  Yüksel Özbay,et al.  Fuzzy clustering complex-valued neural network to diagnose cirrhosis disease , 2011, Expert Syst. Appl..

[41]  M. Usman Akram,et al.  Blood Vessel Enhancement and Segmentation Using Wavelet Transform , 2009, 2009 International Conference on Digital Image Processing.

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

[43]  E. Candès,et al.  Curvelets: A Surprisingly Effective Nonadaptive Representation for Objects with Edges , 2000 .

[44]  M. Kalaivani,et al.  Extraction Of Retinal Blood Vessels Using Curvelet Transform And Kirsch’s Templates , 2012 .

[45]  E. Candès,et al.  New tight frames of curvelets and optimal representations of objects with piecewise C2 singularities , 2004 .

[46]  Rajesh Cherian Roy,et al.  An Improved Retinal Blood Vessel Segmentation Algorithm based on Multistructure Elements Morphology , 2012 .

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

[48]  A Hoover,et al.  Locating blood vessels in retinal images by piece-wise threshold probing of a matched filter response , 1998, AMIA.

[49]  George D. C. Cavalcanti,et al.  An Unsupervised Segmentation Method for Retinal Vessel Using Combined Filters , 2012, 2012 IEEE 24th International Conference on Tools with Artificial Intelligence.

[50]  Hamid Soltanian-Zadeh,et al.  Multi-scale approach for retinal vessel segmentation using medialness function , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[51]  O. Chutatape,et al.  Retinal blood vessel detection and tracking by matched Gaussian and Kalman filters , 1998, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286).

[52]  Ramesh Neelamani,et al.  Adaptive subtraction using complex-valued curvelet transforms , 2010 .