Pixel‐level snakes on the CNNUM: algorithm design, on‐chip implementation and applications

In this paper, a new algorithm for the cellular active contour technique called pixel-level snakes is proposed. The motivation is twofold: on the one hand, a higher efficiency and flexibility in the contour evolution towards the boundaries of interest are pursued. On the other hand, a higher performance and suitability for its hardware implementation onto a cellular neural network (CNN) chip-set architecture are also required. Based on the analysis of previous schemes the contour evolution is improved and a new approach to manage the topological transformations is incorporated. Furthermore, new capabilities in the contour guiding are introduced by the incorporation of inflating/deflating terms based on the balloon forces for the parametric active contours. The entire algorithm has been implemented on a CNN universal machine (CNNUM) chip set architecture for which the results of the time performance measurements are also given. To illustrate the validity and efficiency of the new scheme several examples are discussed including real applications from medical imaging. Copyright © 2005 John Wiley & Sons, Ltd.

[1]  Laurent D. Cohen,et al.  Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  J A Sethian,et al.  A fast marching level set method for monotonically advancing fronts. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[3]  V.M. Brea,et al.  Image segmentation based on active contours using discrete time cellular neural networks , 1998, 1998 Fifth IEEE International Workshop on Cellular Neural Networks and their Applications. Proceedings (Cat. No.98TH8359).

[4]  Tamás Roska,et al.  CNN-based spatio-temporal nonlinear filtering and endocardial boundary detection in echocardiography , 1999 .

[5]  Demetri Terzopoulos,et al.  Deformable models in medical image analysis: a survey , 1996, Medical Image Anal..

[6]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

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

[8]  Ángel Rodríguez-Vázquez,et al.  ACE4k: An analog I/O 64×64 visual microprocessor chip with 7-bit analog accuracy , 2002, Int. J. Circuit Theory Appl..

[9]  Baba C. Vemuri,et al.  Shape Modeling with Front Propagation: A Level Set Approach , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Rachid Deriche,et al.  Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  A. Rodriguez-Vazquez,et al.  A 64/spl times/64 CNN universal chip with analog and digital I/O , 1998, 1998 IEEE International Conference on Electronics, Circuits and Systems. Surfing the Waves of Science and Technology (Cat. No.98EX196).

[12]  J. Sethian,et al.  A Fast Level Set Method for Propagating Interfaces , 1995 .

[13]  Leon O. Chua,et al.  Computing with Front Propagation: Active Contour And Skeleton Models In Continuous-Time CNN , 1999, J. VLSI Signal Process..

[14]  Leon O. Chua,et al.  CNN cloning template: connected component detector , 1990 .

[15]  David López Vilariño,et al.  An Active Contour Algorithm for Continuous-Time Cellular Neural Networks , 1999, J. VLSI Signal Process..

[16]  Liang Zhou,et al.  The detection and quantification of retinopathy using digital angiograms , 1994, IEEE Trans. Medical Imaging.

[17]  Xose Manuel Pardo,et al.  Cellular neural networks and active contours: a tool for image segmentation , 2003, Image Vis. Comput..

[18]  Yan Zhu,et al.  Computerized tumor boundary detection using a Hopfield neural network , 1997, IEEE Transactions on Medical Imaging.

[19]  V. Caselles,et al.  A geometric model for active contours in image processing , 1993 .

[20]  J. A. Sethian,et al.  Fast Marching Methods , 1999, SIAM Rev..

[21]  Xiao Han,et al.  A Topology Preserving Level Set Method for Geometric Deformable Models , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Victor M. Brea,et al.  Discrete-time CNN for image segmentation by active contours , 1998, Pattern Recognit. Lett..

[23]  Max A. Viergever,et al.  Geodesic deformable models for medical image analysis , 1998, IEEE Transactions on Medical Imaging.

[24]  Leon O. Chua,et al.  The CNN paradigm , 1993 .

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