David López Vilariño

Cellular neural networks and active contours: a tool for image segmentation

Abstract In this paper Cellular Neural Networks (CNN) are applied to image segmentation based on active contour techniques. The approach is based on deformable contours which evolve pixel by pixel from their initial shapes and locations until delimiting the objects of interest. The contour shift is guided by external information from the image under consideration which attracts them towards the target characteristics (intensity extremes, edges, etc.) and by internal forces which try to maintain the smoothness of the contour curve. This CNN-based proposal combines the characteristics from implicit and parametric models. As a consequence a high flexibility and control for the evolution dynamics of the snakes are provided, allowing the solution of complex tasks as is the case of the topologic transformations. In addition the proposal is suitable for its implementation as an integrated circuit allowing to take advantages of the massively parallel processing in CNN to reduce processing time.

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

Discrete-time CNN for image segmentation by active contours

In this work we present a new image segmentation strategy which operates by means of active contours implemented on a multilayer cellular neural network. The approach consists of an expanding and thinning process, guided by external information from a contour which evolves until it reaches the final desired position in the image processed.

Implementation of a pixel-level snake algorithm on a CNNUM-based chip set architecture

In this paper, an on-chip implementation of the active contour technique called pixel-level snakes is proposed. This is based on an optimized cellular neural network (CNN) algorithm with capabilities to support changes in the contour topology. The entire algorithm has been implemented on a 64/spl times/64 CNN universal machine chip-set architecture for which the results of the time performance measurements are given. To illustrate the validity and capabilities of the proposed implementation some on-chip experiments are also included.

Design of the processing core of a mixed-signal CMOS DTCNN chip for pixel-level snakes

This paper introduces the processing core of a full-custom mixed-signal CMOS chip intended for an active-contour-based technique, the so-called pixel-level snakes (PLS). Among the different parameters to optimize on the top-down design flow our methodology is focused on area. This approach results in a single-instruction-multiple-data chip implemented by a discrete-time cellular neural network with a correspondence between pixel and processing element. This is the first prototype for PLS; an integrated circuit with a 9/spl times/9 resolution manufactured in a 0.25 -/spl mu/m CMOS STMicroelectronics technology process. Awaiting for experimental results, HSPICE simulations prove the validity of the approach introduced here.

Image segmentation based on active contours using discrete time cellular neural networks

We present a new proposal for image segmentation using deformable models, as an application of discrete-time cellular neural networks (DTCNN). This approach is based on active contours (also called snakes) which evolve until reaching a final desired location. The contours are guided by both external information from the image under consideration which attracts them towards salient characteristics of the scene, and internal energy from the contour image which tries to maintain the smoothness in the curve shape. The massively parallel processing in DTCNN and the use of local information permit a VLSI implementation, suitable for real time applications.

A Cellular Active Contours Algorithm Based on Region Evolution

This paper introduces a new algorithm for implementing cellular active contour technique based on pixel-level snakes (PLS). The main motivation is the optimization of the computational performance, especially when PLS are implemented on pixel-parallel single instruction multiple data (SIMD) processor arrays. The algorithm is based on the evolution of an active region. This allows the implementation of the entire algorithm using every simple local rules. Additionally, nested contours and propagation into narrow cavities are supported. The algorithm and experimental results from real-time implementation on vision chips are presented

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.

Topographic cellular active contour techniques: theory, implementations and comparisons

This paper overviews some massively parallel topographic cellular computational approaches proposed for contour localization and tracking. When implemented on a focal plane cellular array microprocessor, these algorithms offer real-time object contour localization and tracking—even at very high frame rates. Three specific methods (Constrained Wave Computing, Pixel Level Snakes and Moving Patch Method) will be described and compared along with their associated hardware–software architectures. Computational complexity, implementation, and performance related issues are discussed on a common platform (ACE-BOX with the ACEx CNN-UM chips). In conclusion, a novel architecture is proposed incorporating the best solutions learned from this comparative study. Copyright

Design of multilayer discrete time cellular neural networks for image processing tasks based on genetic algorithms

Genetic algorithms are applied to design multilayer discrete-time cellular neural networks for image processing tasks, To this end not only the templates of the different layers will be optimized, but also the network structure itself, that is, number of layers and iterations per layer. As a difference with traditional strategies, both the definition of the optimum network size and the template optimization are done simultaneously.