Abstract
Computed Tomographic Angiography (CTA) has become a popular image modality for the evaluation of arteries and the detection of narrowings. For an objective and reproducible assessment of objects in CTA images, automated segmentation is very important. However, because of the complexity of CTA images it is not possible to find a single parameter setting that results in an optimal segmentation for each possible image of each possible patient. Therefore, we want to find optimal parameter settings for different CTA images. In this paper we investigate the use of Fitness Based Partitioning to find groups of images that require a similar parameter setting for the segmentation algorithm while at the same time evolving optimal parameter settings for these groups. The results show that Fitness Based Partitioning results in better image segmentation than the original default parameter solutions or a single parameter solution evolved for all images.
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References
Bovenkamp, E., Eggermont, J., Li, R., Emmerich, M., Bäck, T., Dijkstra, J., Reiber, J.: Optimizing IVUS Lumen Segmentations using Evolutionary Algorithms. In: The 1st International Workshop on Computer Vision for IntraVascular and IntraCardiac Imaging, October 1–5, pp. 74–81 (2006)
Li, R., Emmerich, M., Eggermont, J., Bovenkamp, E., Bäck, T., Dijkstra, J., Reiber, J.: Mixed-Integer Optimization of Coronary Vessel Image Analysis using Evolution Strategies. In: Keijzer, M., et al. (eds.) Proceedings of the 8th annual conference on Genetic and Evolutionary Computation (GECCO), Seattle, WA, USA, pp. 1645–1652 (2006)
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (1999)
Vanneschi, L., Mauri, G., Valsecchi, A., Cagnoni, S.: Heterogeneous cooperative coevolution: strategies of integration between gp and ga. In: Proceedings of Genetic and Evolutionary Computation Conference, GECCO, Seattle, Washington, USA, July 8-12, pp. 361–368 (2006)
Roberts, M., Claridge, E.: Cooperative coevolution of image feature construction and object detection. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 902–911. Springer, Heidelberg (2004)
Li, R., Eggermont, J., Emmerich, M., Bovenkamp, E., Bäck, T., Dijkstra, J., Reiber, J.: Towards Dynamic Fitness Based Partitioning for IntraVascular UltraSound Image Analysis. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 388–395. Springer, Heidelberg (2007)
Marquering, H., Dijkstra, J., de Koning, P., Stoel, B., Reiber, J.: Towards quantitative analysis of coronary cta. Int. J. Cardiovasc Imaging 21(1), 73–84 (2005)
Emmerich, M., Grötzner, M., Groß, B., Schütz, M.: Mixed-integer evolution strategy for chemical plant optimization with simulators. In: Parmee, I. (ed.) Evolutionary Design and Manufacture - Selected papers from ACDM, pp. 55–67. Springer, London (2000)
Schwefel, H.P.: Evolution and Optimum Seeking. Sixth-Generation Computer Technology Series. Wiley, New York (1995)
Rudolph, G.: An evolutionary algorithm for integer programming. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866, pp. 139–148. Springer, Heidelberg (1994)
Bäck, T.: Evolutionary Algorithms in Theory and Practice. Oxford University Press, New York (1996)
Keijzer, M., Merelo, J.J., Romero, G., Schoenauer, M.: Evolving objects: a general purpose evolutionary computation library. In: EA 2001, Evolution Artificielle, 5th International Conference in Evolutionary Algorithms, pp. 231–244 (2001)
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Eggermont, J. et al. (2008). Optimizing Computed Tomographic Angiography Image Segmentation Using Fitness Based Partitioning. In: Giacobini, M., et al. Applications of Evolutionary Computing. EvoWorkshops 2008. Lecture Notes in Computer Science, vol 4974. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78761-7_28
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DOI: https://doi.org/10.1007/978-3-540-78761-7_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78760-0
Online ISBN: 978-3-540-78761-7
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