Automatic Generation of Synthetic Retinal Fundus Images: Vascular Network

This work is part of an ongoing project aimed to generate synthetic retinal fundus images. This paper concentrates on the generation of synthetic vascular networks with realistic shape and texture characteristics. An example-based method, the Active Shape Model, is used to synthesize reliable vessels’ shapes. An approach based on Kalman Filtering combined with an extension of a Multiresolution Hermite vascular cross-section model has been developed for the simulation of vessels’ textures. The proposed method is able to generate realistic synthetic vascular networks with morphological properties that guarantee the correct flow of the blood and the oxygenation of the retinal surface as observed with fundus cameras. The validity of our synthetic retinal images is demonstrated by qualitative assessment and quantitative analysis.

[1]  Jack Bresenham,et al.  Algorithm for computer control of a digital plotter , 1965, IBM Syst. J..

[2]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[3]  D. Louis Collins,et al.  Design and construction of a realistic digital brain phantom , 1998, IEEE Transactions on Medical Imaging.

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

[5]  Emanuele Trucco,et al.  Leveraging Multiscale Hessian-Based Enhancement With a Novel Exudate Inpainting Technique for Retinal Vessel Segmentation , 2016, IEEE Journal of Biomedical and Health Informatics.

[6]  Dinesh Kumar,et al.  Validating retinal fundus image analysis algorithms: issues and a proposal. , 2013, Investigative ophthalmology & visual science.

[7]  Lucia Ballerini,et al.  Automatic Generation of Synthetic Retinal Fundus Images , 2014, STAG.

[8]  R.M. Haralick,et al.  Statistical and structural approaches to texture , 1979, Proceedings of the IEEE.

[9]  Andrea Giachetti,et al.  OF THE RETINAL VASCULATURE IN FUNDUS IMAGES WITH VAMPIRE , 2015 .

[10]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[11]  Pekka Ruusuvuori,et al.  Computational Framework for Simulating Fluorescence Microscope Images With Cell Populations , 2007, IEEE Transactions on Medical Imaging.

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

[13]  Andrew Hunter,et al.  Measurement of retinal vessel widths from fundus images based on 2-D modeling , 2004, IEEE Transactions on Medical Imaging.

[14]  D. Tegolo,et al.  Novel VAMPIRE algorithms for quantitative analysis of the retinal vasculature , 2013, 2013 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC).

[15]  C. D. Murray.  THE PHYSIOLOGICAL PRINCIPLE OF MINIMUM WORK APPLIED TO THE ANGLE OF BRANCHING OF ARTERIES , 1926, The Journal of general physiology.

[16]  Emanuele Trucco,et al.  Accurate estimation of retinal vessel width using bagged decision trees and an extended multiresolution Hermite model , 2013, Medical Image Anal..

[17]  Salah Bourennane,et al.  Retinal vessel segmentation using a probabilistic tracking method , 2012, Pattern Recognit..

[18]  Alessandra Bertoldo,et al.  Supervised classification of brain tissues through local multi-scale texture analysis by coupling DIR and FLAIR MR sequences , 2012, Medical Imaging.