论文信息 - CNN-based automatic retinal vascular tree extraction

CNN-based automatic retinal vascular tree extraction

The retinal vascular tree has become an important task of medical image processing in different scientific areas. Many studies have focused on developing an automatic algorithm, however little attention has been paid to improve computational processing time of these algorithms. In this paper, an automatic methodology for retinal vascular tree extraction using cellular neural networks (CNNs) is proposed. The aim of using CNNs is to improve computational time in order to achieve real-time requirements.

[1] Csaba Rekeczky,et al. PDE-Based Histogram Modification With Embedded Morphological Processing Of The Level-Sets , 2003, J. Circuits Syst. Comput..

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

[3] Csaba Rekeczky,et al. Pixel‐level snakes on the CNNUM: algorithm design, on‐chip implementation and applications , 2005, Int. J. Circuit Theory Appl..

[4] C. Rekeczky,et al. PDE based histogram modification with embedded morphological processing of the level-sets , 2002, Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications.

[5] Tamás Roska,et al. The CNN universal machine: an analogic array computer , 1993 .

[6] Csaba Rekeczky. MATCNN. Analogic CNN simulation toolbox for MATLAB. Version 1.0. (Research report of the Analogical and Neural Computing Laboratory, DNS-11-1997.) , 1997 .

[7] Lin-Bao Yang,et al. Cellular neural networks: theory , 1988 .

[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] Csaba Rekeczky,et al. Image segmentation and edge detection via constrained diffusion and adaptive morphology: a CNN approach to bubble/debris image enhancement , 1997 .