Unsupervised Method for Retinal Vessel Segmentation Based on Gabor Wavelet and Multiscale Line Detector

Eye and systemic diseases are known to manifest themselves in retinal vasculature. Segmentation of retinal vessel is one of the important steps in retinal image analysis. A simple unsupervised method based on Gabor wavelet and Multiscale Line Detector is proposed for retinal vessel segmentation. Vessels are enhanced by linear superposition of first scale Gabor wavelet image and complemented Green channel. Multiscale Line Detector is used to segment the blood vessels. Finally, a simple post processing scheme based on median filtering is deployed to remove false positives. The proposed scheme was evaluated with publicly available datasets called DRIVE, STARE and HRF, obtaining an accuracy of 0.9470, 0.9472, and 0.9559, and a sensitivity of 0.7421, 0.8004, and 0.7207, respectively. These results are comparable to the state-of-the-art methods, albeit with a simpler approach.

[1]  Augustinus Laude,et al.  Blood vessel segmentation in color fundus images based on regional and Hessian features , 2017, Graefe's Archive for Clinical and Experimental Ophthalmology.

[2]  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.

[3]  Roberto M. Cesar,et al.  Segmentation of Retinal Vasculature Using Wavelets and Supervised Classification: Theory and Implementation , 2009 .

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

[5]  Xin Yang,et al.  Joint Segment-Level and Pixel-Wise Losses for Deep Learning Based Retinal Vessel Segmentation , 2018, IEEE Transactions on Biomedical Engineering.

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

[7]  Matthew B. Blaschko,et al.  A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images , 2017, IEEE Transactions on Biomedical Engineering.

[8]  Stéphane Mallat,et al.  Singularity detection and processing with wavelets , 1992, IEEE Trans. Inf. Theory.

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

[10]  Josien P. W. Pluim,et al.  Robust Retinal Vessel Segmentation via Locally Adaptive Derivative Frames in Orientation Scores , 2016, IEEE Transactions on Medical Imaging.

[11]  Kotagiri Ramamohanarao,et al.  An effective retinal blood vessel segmentation method using multi-scale line detection , 2013, Pattern Recognit..

[12]  Khosro Rezaee,et al.  Optimized clinical segmentation of retinal blood vessels by using combination of adaptive filtering, fuzzy entropy and skeletonization , 2017, Appl. Soft Comput..

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

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

[15]  Keshab K. Parhi,et al.  Blood Vessel Segmentation of Fundus Images by Major Vessel Extraction and Subimage Classification , 2015, IEEE Journal of Biomedical and Health Informatics.

[16]  Stéphane Mallat,et al.  Characterization of Signals from Multiscale Edges , 2011, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  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.

[18]  Krzysztof Krawiec,et al.  Segmenting Retinal Blood Vessels With Deep Neural Networks , 2016, IEEE Transactions on Medical Imaging.

[19]  Yalin Zheng,et al.  Retinal Vessel Segmentation: An Efficient Graph Cut Approach with Retinex and Local Phase , 2015, PloS one.

[20]  Elli Angelopoulou,et al.  Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database , 2013, IET Image Process..

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

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

[23]  Feng Shao,et al.  Discriminative dictionary learning for retinal vessel segmentation using fusion of multiple features , 2019, Signal Image Video Process..

[24]  Vijayan K. Asari,et al.  Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation , 2018, ArXiv.

[25]  Pabitra Mitra,et al.  Ensemble of Deep Convolutional Neural Networks for Learning to Detect Retinal Vessels in Fundus Images , 2016, ArXiv.

[26]  Il Dong Yun,et al.  Deep Vessel Segmentation By Learning Graphical Connectivity , 2018, Medical Image Anal..

[27]  George D. C. Cavalcanti,et al.  Unsupervised Retinal Vessel Segmentation Using Combined Filters , 2016, PloS one.

[28]  Frans Vos,et al.  A model based method for retinal blood vessel detection , 2004, Comput. Biol. Medicine.

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

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

[31]  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.