Energy based medical imaging segmentation methods by using cellular neural networks
Proceedings of the 15th WSEAS international conference on Systems
On the automatic 2d retinal vessel extraction
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Blood vessel segmentation methodologies in retinal images - A survey
Computer Methods and Programs in Biomedicine
Fast texture synthesis with cellular neural network-based patch stitching
International Journal of Circuit Theory and Applications
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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.