Abstract
In this paper, we propose an applicable genetic programming approach to solve the problems of binary image analysis and gray scale image enhancement. Given a section of original image and the corresponding goal image, the proposed algorithm evolves for generations and produces a mathematic morphological operation sequence, and the result performed by which is close to the goal. When the operation sequence is applied to the whole image, the objective of image analysis is achieved. In this sequence, only basic morphological operations— erosion and dilation, and logical operations are used. The well-defined chromosome structure leads brings about more complex morphological operations can be composed in a short sequence. Because of a reasonable evolution strategy, the evolution effectiveness of this algorithm is guaranteed. Tested by the binary image features analysis, this algorithm runs faster and is more accurate and intelligible than previous works. In addition, when this algorithm is applied to infrared finger vein gray scale images to enhance the region of interest, more accurate features are extracted and the accuracy of discrimination is promoted.
This work is supported by National Natural Science Foundation of China (NSFC), under grant number 60875080 and 60673020, and partly supported by the National High Technology Research and Development Program of China (863 Program), with grant number 2007AA01Z453.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ballerini, L., Franzén, L.: Genetic optimization of morphological filters with applications in breast cancer detection. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 250–259. Springer, Heidelberg (2004)
Daida, J.M., Hommes, J.D., Bersano-Begey, T.F., Ross, S.J., Vesecky, J.F.: Algorithm discovery using the genetic programming paradigm: extracting low-contrast curvilinear features from sar images of arctic ice, pp. 417–442 (1996)
Dubuisson, M.-P., Jain, A.: A modified hausdorff distance for object matching. In: Proceedings of the 12th IAPR International Conference on Pattern Recognition, vol. 1, pp. 566–568 (October 1994)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley-Interscience, Hoboken (2000)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley Longman Publishing Co., Inc., Boston (2001)
Harvey, N., Marshall, S.: The use of genetic algorithms in morphological filter design. Signal Processing: Image Communication 8(17), 55–71 (1996)
Hong, J.-H., Cho, S.-B., Cho, U.-K.: A novel evolutionary approach to image enhancement filter design: Method and applications. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 39(6), 1446–1457 (2009)
Huttenlocher, D., Klanderman, G., Rucklidge, W.: Comparing images using the hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(9), 850–863 (1993)
Koza, J.R.: Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge (1992)
Langdon, W.B., Poli, R., McPhee, N.F., Koza, J.R.: Genetic programming: An introduction and tutorial, with a survey of techniques and applications. In: Fulcher, J., Jain, L.C. (eds.) Computational Intelligence: A Compendium. Studies in Computational Intelligence, vol. 115, pp. 927–1028. Springer, Heidelberg (2008)
Munteanu, C., Rosa, A.: Gray-scale image enhancement as an automatic process driven by evolution. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 34(2), 1292–1298 (2004)
Poli, R.: Genetic programming for image analysis. In: GECCO 1996: Proceedings of the First Annual Conference on Genetic Programming, pp. 363–368. MIT Press, Cambridge (1996)
Quintana, M.I., Poli, R., Claridge, E.: On two approaches to image processing algorithm design for binary images using GP. In: Raidl, G.R., Cagnoni, S., Cardalda, J.J.R., Corne, D.W., Gottlieb, J., Guillot, A., Hart, E., Johnson, C.G., Marchiori, E., Meyer, J.-A., Middendorf, M. (eds.) EvoWorkshops 2003. LNCS, vol. 2611, pp. 422–431. Springer, Heidelberg (2003)
Quintana, M.I., Poli, R., Claridge, E.: Morphological algorithm design for binary images using genetic programming. Genetic Programming and Evolvable Machines 7(1), 81–102 (2006)
Soille, P.: Morphological Image Analysis: Principles and Applications. Springer-Verlag New York, Inc., Secaucus (2003)
Tackett, W.A.: Genetic programming for feature discovery and image discrimination. In: Proceedings of the 5th International Conference on Genetic Algorithms, pp. 303–311. Morgan Kaufmann Publishers Inc., San Francisco (1993)
Weaver, A.: Biometric authentication. Computer 39(2), 96–97 (2006)
Yoda, I., Yamamoto, K., Yamada, H.: Automatic acquisition of hierarchical mathematical morphology procedures by genetic algorithms. Image Vision Comput. 17(10), 749–760 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, J., Tan, Y. (2011). A Novel Genetic Programming Algorithm for Designing Morphological Image Analysis Method. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds) Advances in Swarm Intelligence. ICSI 2011. Lecture Notes in Computer Science, vol 6728. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21515-5_65
Download citation
DOI: https://doi.org/10.1007/978-3-642-21515-5_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21514-8
Online ISBN: 978-3-642-21515-5
eBook Packages: Computer ScienceComputer Science (R0)