Retinal blood vessel extraction using tunable bandpass filter and fuzzy conditional entropy

BACKGROUND AND OBJECTIVES Extraction of blood vessels on retinal images plays a significant role for screening of different opthalmologic diseases. However, accurate extraction of the entire and individual type of vessel silhouette from the noisy images with poorly illuminated background is a complicated task. To this aim, an integrated system design platform is suggested in this work for vessel extraction using a sequential bandpass filter followed by fuzzy conditional entropy maximization on matched filter response. METHODS At first noise is eliminated from the image under consideration through curvelet based denoising. To include the fine details and the relatively less thick vessel structures, the image is passed through a bank of sequential bandpass filter structure optimized for contrast enhancement. Fuzzy conditional entropy on matched filter response is then maximized to find the set of multiple optimal thresholds to extract the different types of vessel silhouettes from the background. Differential Evolution algorithm is used to determine the optimal gain in bandpass filter and the combination of the fuzzy parameters. Using the multiple thresholds, retinal image is classified as the thick, the medium and the thin vessels including neovascularization. RESULTS Performance evaluated on different publicly available retinal image databases shows that the proposed method is very efficient in identifying the diverse types of vessels. Proposed method is also efficient in extracting the abnormal and the thin blood vessels in pathological retinal images. The average values of true positive rate, false positive rate and accuracy offered by the method is 76.32%, 1.99% and 96.28%, respectively for the DRIVE database and 72.82%, 2.6% and 96.16%, respectively for the STARE database. Simulation results demonstrate that the proposed method outperforms the existing methods in detecting the various types of vessels and the neovascularization structures. CONCLUSIONS The combination of curvelet transform and tunable bandpass filter is found to be very much effective in edge enhancement whereas fuzzy conditional entropy efficiently distinguishes vessels of different widths.

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

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

[3]  Bradley M. Hemminger,et al.  Contrast Limited Adaptive Histogram Equalization image processing to improve the detection of simulated spiculations in dense mammograms , 1998, Journal of Digital Imaging.

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

[5]  Marios S. Pattichis,et al.  Recent multiscale AM-FM methods in emerging applications in medical imaging , 2012, EURASIP J. Adv. Signal Process..

[6]  Alfred Mertins,et al.  Segmentation of retinal vessels with a hysteresis binary-classification paradigm , 2012, Comput. Medical Imaging Graph..

[7]  Wenbing Tao,et al.  Image segmentation by three-level thresholding based on maximum fuzzy entropy and genetic algorithm , 2003, Pattern Recognit. Lett..

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

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

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

[11]  Shadi AlZu'bi,et al.  Multiresolution Analysis Using Wavelet, Ridgelet, and Curvelet Transforms for Medical Image Segmentation , 2011, Int. J. Biomed. Imaging.

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

[13]  Chikkannan Eswaran,et al.  An Automated Blood Vessel Segmentation Algorithm Using Histogram Equalization and Automatic Threshold Selection , 2011, Journal of Digital Imaging.

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

[15]  P. N. Suganthan,et al.  Differential Evolution: A Survey of the State-of-the-Art , 2011, IEEE Transactions on Evolutionary Computation.

[16]  Edgardo Manuel Felipe Riverón,et al.  Regular paper , 2005, 2005 IEEE/PES Transmission & Distribution Conference & Exposition: Asia and Pacific.

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

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

[19]  Santi P. Maity,et al.  Retinal blood vessel extraction using curvelet transform and conditional fuzzy entropy , 2014, 2014 22nd European Signal Processing Conference (EUSIPCO).

[20]  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).

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

[22]  Zhongzhi Shi,et al.  Studies on Fuzzy Information Measures , 2006, 2006 5th IEEE International Conference on Cognitive Informatics.

[23]  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.

[24]  Dalwinder Singh,et al.  A new morphology based approach for blood vessel segmentation in retinal images , 2014, 2014 Annual IEEE India Conference (INDICON).

[25]  Giri Babu Kande,et al.  Unsupervised Fuzzy Based Vessel Segmentation In Pathological Digital Fundus Images , 2010, Journal of Medical Systems.

[26]  Evangelos Dermatas,et al.  Multi-scale retinal vessel segmentation using line tracking , 2010, Comput. Medical Imaging Graph..

[27]  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.

[28]  J. George,et al.  Fast Adaptive Anisotropic Filtering for Medical Image Enhancement , 2008, 2008 IEEE International Symposium on Signal Processing and Information Technology.

[29]  Emmanuel J. Candès,et al.  The curvelet transform for image denoising , 2002, IEEE Trans. Image Process..

[30]  Xiaohui Liu,et al.  Segmentation of the Blood Vessels and Optic Disk in Retinal Images , 2014, IEEE Journal of Biomedical and Health Informatics.

[31]  Muhammad Younus Javed,et al.  Detection of neovascularization in retinal images using multivariate m-Mediods based classifier , 2013, Comput. Medical Imaging Graph..

[32]  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.

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

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

[35]  Roberto Marcondes Cesar Junior,et al.  Retinal Vessel Segmentation Using the 2-D Morlet Wavelet and Supervised Classification , 2005, ArXiv.

[36]  Hong Shen,et al.  Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms , 1999, IEEE Transactions on Information Technology in Biomedicine.

[37]  K. B. Khanchandani,et al.  Retinal blood vessel segmentation using graph cut analysis , 2015, 2015 International Conference on Industrial Instrumentation and Control (ICIC).

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

[39]  Hossein Rabbani,et al.  Extraction of retinal blood vessels by curvelet transform , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[40]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[41]  Santi P. Maity,et al.  Extraction of Retinal Blood Vessel Using Curvelet Transform and Fuzzy C-Means , 2014, 2014 22nd International Conference on Pattern Recognition.

[42]  Marios S. Pattichis,et al.  Detection of neovascularization in the optic disc using an AM-FM representation, granulometry, and vessel segmentation , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

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