New deformable models development using the MESA environment

• Autorzy: Bołdak C. , Reska D. , Krętowski M.
• Języki publikacji: EN

Abstrakty

EN

In this work we present capabilities of a new environment called Medical Segmentation Arena (MESA) in developing new segmentation methods based on the deformable models. The MESA environment was created in frame of the project “Information Platform TEWI” to facilitate researches in the medical image processing domain. The operator can formulate new segmentation algorithms based on the deformable models theory (active contours - snakes) by composing them from ready-to-use blocks. He can also develop new blocks with a simple Java-based programming mechanism. Then he can easily evaluate these algorithms with many o ff ered tools (image management and visualization, batch experiment planning and running, parametric studies, virtual phantom generation, segmentation quality assessment, distributing of computations). We give also some examples of the snake energies and models implemented in the MESA environment presenting its capabilities in practice.

Słowa kluczowe

EN

image segmentation; deformable models; segmentation methods development; image processing environment; Java

PL

segmentacja obrazu; przetwarzanie obrazu; model deformowalny; Java

• Wydawca: Wydawnictwo Politechniki Łódzkiej
• Czasopismo: Journal of Applied Computer Science
• Rocznik: 2014
• Tom: Vol. 22, nr 1
• Strony: 29--48
• Opis fizyczny: Bibliogr. 10 poz.

Twórcy

autor

Bołdak C.

• Bialystok University of Technology, Faculty of Computer Science, Wiejska 45A, 15-351 Bialystok

autor

Reska D.

• Bialystok University of Technology, Faculty of Computer Science, Wiejska 45A, 15-351 Bialystok

autor

Krętowski M.

• Bialystok University of Technology, Faculty of Computer Science, Wiejska 45A, 15-351 Bialystok

Bibliografia

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• [4] Xu, C. and Prince, J. L., Snakes, Shapes, and Gradient Vector Flow, IEEE Transactions on Image Processing, Vol. 7, No. 3, 1998, pp. 359–369.
• [5] Gunn, S. and Nixon, M., A robust snake implementation; a dual active contour, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, 1997, pp. 63–68.
• [6] Mcinerney, T. and Terzopoulos, D., T-Snakes: Topology Adaptive Snakes, Medical Image Analysis, 1999, pp. 840–845.
• [7] Sethian, J., Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science, Cambridge University Press, 1999.
• [8] Jalba, A., Wilkinson, M., and Roerdink, J., CPM: A Deformable Model for Shape Recovery and Segmentation Based on Charged Particles, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 26, No. 10, 2004, pp. 1320–1335.
• [9] Reska, D., Jurczuk, K., Boldak, C., and Kretowski, M., MESA: MEdical Segmentation Arena environment, http://mesa.wi.pb.edu.pl/.
• [10] Reska, D., Boldak, C., and Kretowski, M., A distributed approach for development of deformable model-based segmentation methods (accepted), Image Processing and Communications, 2013.