Automatic vessel segmentation on fundus images using vessel filtering and fuzzy entropy

Vessel segmentation is a critical and challenging task for fundus image processing, which is precursor and essential first step to further vessel measurement and diagnosis. This paper proposes a novel hybrid automatic vessel segmentation method for the delineation of vessels on fundus images. The method consists of two main steps including Hessian-based vessel filtering and vessel segmentation. In vessel filtering, multi-scale linear filtering based on Hessian matrix is adapted to enhance vessels in the image. After vessel filtering, a novel two-dimensional histogram of filtering image is generated. Then, the thresholds are determined by the fuzzy entropic concepts. We demonstrate the effectiveness of the proposed method on real fundus images from DRIVE database. Quantification analysis is applied through three metrics with respect to manual delineated ground truth from one specialist. Compared to three other methods, the proposed method yields more complete and accurate results.

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

[2]  Alejandro F. Frangi,et al.  Muliscale Vessel Enhancement Filtering , 1998, MICCAI.

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

[4]  Qing Tian,et al.  Cross-heterogeneous-database age estimation through correlation representation learning , 2017, Neurocomputing.

[5]  Yong Yang,et al.  An Automatic Hybrid Method for Retinal Blood Vessel Extraction , 2008, Int. J. Appl. Math. Comput. Sci..

[6]  Changming Sun,et al.  Junction detection for linear structures based on Hessian, correlation and shape information , 2012, Pattern Recognit..

[7]  Alexander Rath,et al.  Multiscale Hessian fracture filtering for the enhancement and segmentation of narrow fractures in 3D image data , 2013, Comput. Geosci..

[8]  Xiaoxia Yin,et al.  Accurate Image Analysis of the Retina Using Hessian Matrix and Binarisation of Thresholded Entropy with Application of Texture Mapping , 2014, PloS one.

[9]  Shiguo Lian,et al.  Forensics feature analysis in quaternion wavelet domain for distinguishing photographic images and computer graphics , 2017, Multimedia Tools and Applications.

[10]  Xiaoyi Jiang,et al.  Adaptive Local Thresholding by Verification-Based Multithreshold Probing with Application to Vessel Detection in Retinal Images , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Ahmed S. Abutableb.  Automatic thresholding of gray-level pictures using two-dimensional entropy , 1989 .

[12]  Yuhui Zheng,et al.  Image segmentation by generalized hierarchical fuzzy C-means algorithm , 2015, J. Intell. Fuzzy Syst..

[13]  Chengsheng Yuan,et al.  Fingerprint liveness detection based on multi-scale LPQ and PCA , 2016, China Communications.

[14]  Xingming Sun,et al.  Effective and Efficient Global Context Verification for Image Copy Detection , 2017, IEEE Transactions on Information Forensics and Security.

[15]  Xingming Sun,et al.  Segmentation-Based Image Copy-Move Forgery Detection Scheme , 2015, IEEE Transactions on Information Forensics and Security.

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

[17]  H. D. Cheng,et al.  Thresholding using two-dimensional histogram and fuzzy entropy principle , 2000, IEEE Trans. Image Process..

[18]  Zhili Zhou,et al.  Fast and accurate near-duplicate image elimination for visual sensor networks , 2017, Int. J. Distributed Sens. Networks.

[19]  Hugues Talbot,et al.  Filtering and segmentation of 3D angiographic data: Advances based on mathematical morphology , 2013, Medical Image Anal..

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

[21]  Gang Chen,et al.  Color Image Analysis by Quaternion-Type Moments , 2014, Journal of Mathematical Imaging and Vision.

[22]  Tony Lindeberg,et al.  Scale-Space Theory in Computer Vision , 1993, Lecture Notes in Computer Science.

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

[24]  David Zhang,et al.  A Modified Matched Filter With Double-Sided Thresholding for Screening Proliferative Diabetic Retinopathy , 2009, IEEE Transactions on Information Technology in Biomedicine.

[25]  Ling Shao,et al.  A rapid learning algorithm for vehicle classification , 2015, Inf. Sci..

[26]  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.

[27]  Shankar M. Krishnan,et al.  Detection and measurement of retinal vessels in fundus images using amplitude modified second-order Gaussian filter , 2002, IEEE Transactions on Biomedical Engineering.

[28]  Changming Sun,et al.  A new method for linear feature and junction enhancement in 2D images based on morphological operation, oriented anisotropic Gaussian function and Hessian information , 2014, Pattern Recognit..

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

[30]  Philip J. Morrow,et al.  Algorithms for digital image processing in diabetic retinopathy , 2009, Comput. Medical Imaging Graph..

[31]  Manuel G. Penedo,et al.  A Snake for Retinal Vessel Segmentation , 2007, IbPRIA.

[32]  Alejandro F. Frangi,et al.  Quantitative analysis of vascular morphology from 3D MR angiograms: In vitro and in vivo results , 2001, Magnetic resonance in medicine.

[33]  Zhihua Xia,et al.  Steganalysis of least significant bit matching using multi-order differences , 2014, Secur. Commun. Networks.