Abstract
The segmentation of Computed Tomography images is an extremely challenging task due to the heterogeneities of the regions, their textured intensities, the presence of noise and the blurred edges which define the limits of the different areas. In this work, we propose a method for obtaining a segmentation of the abdominal region in Computed Tomography images based on the analysis of the global distribution of the intensity values in the whole data set and the local distribution within the neighborhood of each pixel. The global information is used to obtain an initial approximation, whereas the local data are analyzed to extract a much more accurate and complete segmentation. The combination of both scales allows obtaining a quite satisfactory segmentation in an automatic way.
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References
Grady, L.: Random walks for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 28(11), 1768–1783 (2006)
Okada, T., Yokota, K., Hori, M., Nakamoto, M., Nakamura, H., Sato, Y.: Construction of hierarchical multi-organ statistical atlases and their application to multi-organ segmentation from CT images. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 502–509. Springer, Heidelberg (2008)
Couprie, C., Grady, L.J., Najman, L., Talbot, H.: Anisotropic diffusion using power watersheds. In: Proceedings of the International Conference on Image Processing, ICIP 2010, 26–29 September, Hong Kong, China, pp. 4153–4156 (2010)
Chan, T.F., Sandberg, B.Y., Vese, L.A.: Active contours without edges for vector-valued images. J. Vis. Commun. Image Represent. 11(2), 130–141 (2000)
Cremers, D., Tischhäuser, F., Weickert, J., Schnörr, C.: Diffusion snakes: introducing statistical shape knowledge into the mumford-shah functional. Int. J. Comput. Vis. 50(3), 295–313 (2002)
Sagiv, C., Sochen, N.A., Zeevi, Y.Y.: Integrated active contours for texture segmentation. IEEE Trans. Image Process. 15(6), 1633–1646 (2006)
Alemán-Flores, M., Álvarez-León, L., Caselles, V.: Texture-oriented anisotropic filtering and geodesic active contours in breast tumor ultrasound segmentation. J. Math. Imaging Div. 28(1), 81–97 (2007)
Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)
Álvarez, L., Baumela, L., Márquez-Neila, P., Henríquez, P.: A real time morphological snakes algorithm. IPOL J. 2, 1–7 (2012)
Álvarez, L., Baumela, L., Henríquez, P., Márquez-Neila, P.: Morphological snakes. In: The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010, San Francisco, CA, USA, 13–18 June 2010, pp. 2197–2202 (2010)
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Alemán-Flores, M., Alemán-Flores, P., Fuentes-Pavón, R. (2015). Analysis of Global and Local Intensity Distributions for the Segmentation of Computed Tomography Images. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2015. EUROCAST 2015. Lecture Notes in Computer Science(), vol 9520. Springer, Cham. https://doi.org/10.1007/978-3-319-27340-2_62
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DOI: https://doi.org/10.1007/978-3-319-27340-2_62
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