Blood vessel segmentation in retinal images using echo state networks

We propose a novel supervised technique for blood vessel segmentation in retinal images based on echo state networks. Retinal vessel segmentation is widely used for numerous clinical purposes such as the detection of various cardiovascular and ophthalmologic diseases. A large number of retinal vessel segmentation methods have been reported, yet achieving accurate and efficient vessel segmentation still remains a challenge. Recently, reservoir computing has drawn much attention as a new computing framework based on recurrent neural networks. The Echo State Network (ESN), which uses neural nodes as the computing elements of the recurrent network, represents one of the efficient learning models of reservoir computing. This paper investigates the viability of echo state networks for blood vessel segmentation in retinal images. Initial image features are projected onto the echo state network reservoir which maps them, through its internal nodes activations, into a new set of features to be classified into vessel or non-vessel by the echo state network readout which consists, in the proposed approach, of a multi-layer perceptron. Experimental results on the publicly available DRIVE dataset, commonly used in retinal vessel segmentation research, demonstrate the ability of the proposed method in achieving promising performance results in terms of both segmentation accuracy and efficiency.

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

[2]  Vasileios Megalooikonomou,et al.  Discriminative vessel segmentation in retinal images by fusing context-aware hybrid features , 2014, Machine Vision and Applications.

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

[4]  Xiaohong W. Gao,et al.  A method of vessel tracking for vessel diameter measurement on retinal images , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[5]  Aliaa A. A. Youssif,et al.  Optic Disc Detection From Normalized Digital Fundus Images by Means of a Vessels' Direction Matched Filter , 2008, IEEE Transactions on Medical Imaging.

[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]  Manuel G. Penedo,et al.  Personal authentication using digital retinal images , 2006, Pattern Analysis and Applications.

[8]  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.

[9]  Shan Liu,et al.  An effective multivariate time series classification approach using echo state network and adaptive differential evolution algorithm , 2016, Expert Syst. Appl..

[10]  Benjamin Schrauwen,et al.  An experimental unification of reservoir computing methods , 2007, Neural Networks.

[11]  Kevin Curran,et al.  An experimental evaluation of echo state network for colour image segmentation , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

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

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

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

[15]  Walid Saad,et al.  Optimized uplink-downlink decoupling in LTE-U networks: An echo state approach , 2016, 2016 IEEE International Conference on Communications (ICC).

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

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

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

[19]  George K. Matsopoulos,et al.  Multimodal registration of retinal images using self organizing maps , 2004, IEEE Transactions on Medical Imaging.

[20]  Henry Markram,et al.  Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations , 2002, Neural Computation.

[21]  Tanish Zaveri,et al.  Deep learning feature map for content based image retrieval system for remote sensing application , 2016 .

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

[23]  Manuel G. Penedo,et al.  Retinal vessel tree segmentation using a deformable contour model , 2008, 2008 19th International Conference on Pattern Recognition.

[24]  Abdelkader Benyettou,et al.  Novel Approach Using Echo State Networks for Microscopic Cellular Image Segmentation , 2016, Cognitive Computation.

[25]  Sheifali Gupta,et al.  Retinal Blood Vessel Segmentation Algorithms: A Comparative Survey , 2016 .

[26]  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.

[27]  Herbert Jaeger,et al.  Reservoir computing approaches to recurrent neural network training , 2009, Comput. Sci. Rev..

[28]  Petia Koprinkova-Hristova,et al.  Clustering of spectral images using Echo state networks , 2013, 2013 IEEE INISTA.

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

[30]  V. K. Govindan,et al.  A Review of Computer Aided Detection of Anatomical Structures and Lesions of DR from Color Retina Images , 2015 .

[31]  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.

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