Abstract
Genetic Programming is a method of program discovery/optimisation consisting of a special kind of genetic algorithm capable of operating on nonlinear chromosomes (parse trees) representing programs and an interpreter which can run the programs being optimised. In this paper we describe a set of terminals and functions for the parse trees handled by genetic programming which enable it to develop effective image filters. These filters can either be used to highly enhance and detect features of interest or to build pixel-classification-based segmentation algorithms. Some experiments with medical images which show the efficacy of the approach are reported.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
David Andre. Automatically defined features: The simultaneous evolution of 2-dimensional feature detectors and an algorithm for using them. In Kenneth E. Kinnear, Jr., editor, Advances in Genetic Programming, chapter 23. MIT Press, 1994.
John Francis Canny. Finding edges and lines in images. Technical Report 720, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, June 1983.
G. Coppini, M. Demi, R. Poli, and G. Valli. An artificial vision system for X-ray images of human coronary trees. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(2):156–162, 1993.
G. Coppini, R. Poli, M. Rucci, and G. Valli. A neural network architecture for understanding 3D scenes in medical imaging. Computer and Biomedical Research, 25:569–585, 1992.
Lawrence O. Hall, Amine M. Bensaid, Laurence P. Clarke, Robert P. Velthuizen, Martin S. Silbuger, and James C. Bezdek. A comparison on neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE Transactions on Neural Networks, 3(5):672–682, 1992.
Christopher Harris and Bernard Buxton. Evolving edge detectors. Research Note RN/96/3, UCL, Gower Street, London, WC1E 6BT, UK, January 1996.
Michael Patrick Johnson, Pattie Maes, and Trevor Darrell. Evolving visual routines. In Rodney A. Brooks and Pattie Maes, editors, ARTIFICIAL LIFE IV, Proceedings of the fourth International Workshop on the Synthesis and Simulation of Living Systems, pages 198–209, MIT, Cambridge, MA, USA, 6–8 July 1994. MIT Press.
John R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, 1992.
John R. Koza. Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Pres, Cambridge, Massachusetts, 1994.
Walter Alden Tackett. Genetic programming for feature discovery and image discrimination. In International Conference on Genetic Algorithms, 1993.
Astro Teller and Manuela Veloso. PADO: Learning tree structured algorithms for orchestration into an object recognition system. Technical Report CMU-CS-95-101, Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA, 1995.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1996 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Poli, R. (1996). Genetic Programming for feature detection and image segmentation. In: Fogarty, T.C. (eds) Evolutionary Computing. AISB EC 1996. Lecture Notes in Computer Science, vol 1143. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0032777
Download citation
DOI: https://doi.org/10.1007/BFb0032777
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-61749-5
Online ISBN: 978-3-540-70671-7
eBook Packages: Springer Book Archive