Abstract
We review more than 200 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, structure-level, 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 the 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.
| Original language | English |
|---|---|
| Journal | Pattern Recognition |
| Volume | 35 |
| Issue number | 10 |
| Pages (from-to) | 2279-2301 |
| Number of pages | 23 |
| ISSN | 0031-3203 |
| DOIs | |
| Publication status | Published - 01.01.2002 |
Funding
This work was partly supported by the Dutch Cancer Foundation (KWF) on grant RUL-97-1509, the Foundation for Computer Science Research in the Netherlands (SION) and the Dutch Organisation for Scientific Research (NWO). We are highly grateful to R.P.W. Duin, J. Kittler and L.J. van Vliet for commenting on an earlier, draft version of this manuscript. We also thank an anonymous referee for useful remarks and suggestions. About the Author —M. EGMONT-PETERSEN was born in Copenhagen, Denmark, in 1967. He received the combined B.S. and combined M.S. degrees in Computer Science/Business Administration from Copenhagen Business School in 1988 and 1990, respectively. He received the Ph.D. degree in Medical Informatics from Maastricht University, The Netherlands, in 1996. He has worked from 1997 to 2000 for the Division of Image Processing, Department of Radiology, Leiden University Medical Centre, as postdoctoral researcher. He is currently associated with the Department of Computer Science at the University of Utrecht, The Netherlands. Dr. Egmont-Petersen is currently developing novel learning algorithms for Bayesian Belief Networks. His main research interests include belief networks, neural networks, support vector machines, statistical classifiers, feature selection, image understanding and invariant theory. He has published more than 35 papers in journals and conference proceedings. About the Author —D. DE RIDDER received his M.Sc. degree in 1996 from the Department of Computer Science of the Delft University of Technology, The Netherlands, where he is currently a Ph.D. student in the Pattern Recognition Group at the Department of Applied Physics. His research interests include statistical pattern recognition, image processing and in particular the application of neural network techniques in the field of non-linear image processing. Currently he is working on developing and extending tools for non-linear data analysis. He has written over 20 papers in journals and conference proceedings. About the Author —RER. NAT. HABIL. HEINZ HANDELS was born in Würselen, Germany, in 1960. After his study of Informatics, Mathematics and Physics he received his Ph.D. degree in Computer Science at the RWTH Aachen, Germany, in 1992. Since 1992 he is the head of the Medical Image Processing and Pattern Recognition Group of the Institute for Medical Informatics at the Medical University of Lübeck. In 1999, he finished his Habilitation for Medical Informatics at the Medical University of Lübeck. His current research interest is focussed on the development of methods and optimised systems for the analysis and recognition of pathological image structures like tumours in radiological and dermatological images. This includes the integration of image analysis algorithms and feature selection methods with neural pattern recognition techniques for medical decision support. He has published more than 70 papers in journals and conference proceedings.
