Abstract
The Image Foresting Transform (IFT) is a framework for seeded image segmentation, based on the computation of minimal cost paths in a discrete representation of an image. In two recent publications, we have shown that the segmentations obtained by the IFT may be improved by refining the segmentation locally around the boundaries between segmented regions. Since these methods operate on a small sub-set of the image elements only, they may be implemented efficiently if the set of boundary elements is known. Here, we show that this set may be obtained on-the-fly, at virtually no additional cost, as a by-product of the IFT algorithm.
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Adams, R., Bischof, L.: Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(6), 641–647 (1994)
Audigier, R., Lotufo, R., Falcão, A.: On integrating iterative segmentation by watershed with tridimensional visualization of MRIs. In: Proceedings of the Computer Graphics and Image Processing, XVII Brazilian Symposium, pp. 130–137. IEEE Computer Society, Los Alamitos (2004)
Boykov, Y., Funka-Lea, G.: Graph cuts and efficient N-D image segmentation. International Journal of Computer Vision 70(2), 109–131 (2006)
Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische Mathematik 1, 269–271 (1959)
Falcão, A.X., Bergo, F.P.: Interactive volume segmentation with differential image foresting transforms. IEEE Transactions on Medical Imaging 23(9), 1100–1108 (2004)
Falcão, A.X., Stolfi, J., Lotufo, R.A.: The image foresting transform: Theory, algorithms, and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(1), 19–29 (2004)
Grady, L.: Random walks for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(11), 1768–1783 (2006)
Lorensen, W.E.: Marching cubes: A high resolution 3D surface construction algorithm. Computer Graphics 21(4), 163–169 (1987)
Malmberg, F., Lindblad, J., Nyström, I.: Sub-pixel segmentation with the image foresting transform. In: Wiederhold, P., Barneva, R.P. (eds.) IWCIA 2009. LNCS, vol. 5852, pp. 201–211. Springer, Heidelberg (2009)
Malmberg, F., Nyström, I., Mehnert, A., Engstrom, C., Bengtsson, E.: Relaxed image foresting transforms for interactive volume image segmentation. In: Dawant, B.M., Haynor, D.R. (eds.) Proceedings of SPIE Medical Imaging, vol. 7623. SPIE, San Jose (2010)
Mehnert, A.J.H., Jackway, P.T.: An improved seeded region growing algorithm. Pattern Recognition Letters 18, 1065–1071 (1997)
Sladoje, N., Lindblad, J.: Estimation of moments of digitized objects with fuzzy borders. In: Roli, F., Vitulano, S. (eds.) ICIAP 2005. LNCS, vol. 3617, pp. 188–195. Springer, Heidelberg (2005)
Sladoje, N., Lindblad, J.: High-precision boundary length estimation by utilizing gray-level information. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(2), 357–363 (2009)
Sladoje, N., Lindblad, J.: Pixel coverage segmentation for improved feature estimation. In: Foggia, P., Sansone, C., Vento, M. (eds.) ICIAP 2009. LNCS, vol. 5716, pp. 929–938. Springer, Heidelberg (2009)
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Malmberg, F. (2011). Image Foresting Transform: On-the-Fly Computation of Segmentation Boundaries. In: Heyden, A., Kahl, F. (eds) Image Analysis. SCIA 2011. Lecture Notes in Computer Science, vol 6688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21227-7_57
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DOI: https://doi.org/10.1007/978-3-642-21227-7_57
Publisher Name: Springer, Berlin, Heidelberg
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