We review more than two hundred applications of neural networks in image processing and discuss the present and possible future role of neural networks, especially feed-forward neural networks, Kohonen feature maps and Hopfield neural networks. The various applications are categorised into a novel two-dimensional taxonomy for image processing algorithms. One dimension specifies the type of task performed by the algorithm: preprocessing, data reduction/feature extraction, segmentation, object recognition, image understanding and optimisation. The other dimension captures the abstraction level of the input data processed by the algorithm: pixel-level, local feature-level, structurelevel, object-level, object-set level and scene characterisation. Each of the six types of tasks poses specific constraints to a neural-based approach. These specific conditions are discussed in detail. A synthesis is made of unresolved problems related to application of pattern recognition techniques in image processing and specifically to the application of neural networks. Finally, we present an outlook into the future application of neural networks and relate them to novel developments. Keywords: neural networks; digital image processing; invariant pattern recognition; preprocessing; feature extraction; image compression; segmentation; object recognition; image understanding; optimization. * Corresponding author. M. Egmont-Petersen, Institute of Information and Computing Sciences, Utrecht University, P.O.B. 80.089, 3508 TB Utrecht, The Netherlands. Email: michael@cs.uu.nl. WWW: Http://www.cs.uu.nl/people/michael/nn-review.html.

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An original method for an automatic detection of contours in difficult images is proposed. This method is based on a tight cooperation between a multi-resolution neural network and a hidden Markov model-enhanced dynamic programming procedure. This new method is able to overcome the three major drawbacks of the "standard" active contours: initialization dependancy, exclusive use of local information and occlusion sensitivity. The driving idea is to introduce high-order a priori information in each step of the system. An application to the automatic detection of the left ventricle in digital X-ray images is proposed. 1. Introduction Edge extraction, automatic contour detection are basic problems in computer vision. This task is particularly difficult for low-contrast and noisy images common in medical applications. Furthermore, a robust automatic procedure has to cope with the wide intrinsic variability of medical images. Several authors have modeled the problem of contour recovery as ...