Abstract
Most image segmentation techniques are based on the intensity homogeneity. Intensity inhomogeneity frequently occurs in real word image like medical images. This type of images fails to provide accurate segmentation result; this is challenging issue. In this paper, we present a robust region-based method for image segmentation, which is able to deal with intensity inhomogeneities in the images. This method derives a local intensity clustering property of the image based on the model of images with intensity inhomogeneities, and then defines a local clustering criterion function in the neighborhood of each point. In a level set formulation, this criterion defines energy in terms of the level set function and a bias field. The level set functions represent a partition of the image domain whereas a bias field accounts for the intensity inhomogeneity of the image. Therefore by minimizing this energy, our proposed method is able to simultaneously segment the image and estimate he bias field, and the estimated bias field can be used for intensity inhomogeneity correction. Finally, experiments on some medical images have demonstrated the efficiency and robustness of the presented model.
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References
Guo, D., Ming, X.: Color clustering and learning for image segmentation based on neural networks. IEEE Trans. Neural Netw. 16(4), 925–936 (2005)
Li, C., Xu, C., Gui, C., Fox, M D.: Level set evolution without re-initialization: a new variational formulation. In: Proceedings of IEEE Conference Computer Vision and Pattern Recognition, vol. 1, pp. 430–436 (2005)
Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int. J. Comput. Vision 22(1), 61–79 (1997)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vision 1(4), 321–331 (1988)
Osher, S., Sethian, J.A.: Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. J. Comput. Phys. 79(1), 12–49 (1988)
Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)
Tsai, A.Y., Willsky, A.S.: Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification. IEEE Trans. Image Process. 10(8), 1169–1186 (2001)
Paragios, N., Deriche, R.: Geodesic active regions and level set methods for supervised texture segmentation. Int. J. Comput. Vis. 46(4), 223–247 (2002)
Gao, S., Bui, T.D.: Image segmentation and selective smoothing by using Mumford-Shah model. IEEE Trans. Image Process. 14(10), 1537–1549 (2005)
Glover, G.H., Hayes, C.E., Pelc, N.J.: Comparison of linear and circular polarization for magnetic resonance imaging. J. Magn. Reson. 64(2), 255–270 (1985)
Harvey, I., Tofts, P.S., Morris, J.K., Wicks, D.A.G., Ron, M.A.: Sources of T1 variance in normal human white matter. Magn. Reson. Imaging 9(1), 53–59 (1991)
Simmons, A., Tofts, P.S., Barker, G.J., Arridge, S.R.: Sources of intensity nonuniformity in spin echo images at 1.5T. Magn. Reson. Med. 32(1), 121–128 (1994)
Narayana, P.A., Brey, W.W., Kulkarni, M.V., Sievenpiper, C.L.: Compensation for surface coil sensitivity variation in magnetic resonance imaging. Magn. Reson. Imaging 6(3), 271–274 (1988)
Brey, W.W., Narayana, P.A.: Correction for intensity falloff in surface coil magnetic resonance imaging. Med. Phys. 15(2), 241–245 (1988)
Stollberger, R., Wach, P.: Imaging of the active b1 field in vivo. Magn. Reson. Med. 35(2), 246–251 (1996)
McVeigh, E.R., Bronskill, M.J., Henkelman, R.M.: Phase and sensitivity of receiver coils in magnetic resonance imaging. Med. Phys. 13(6), 806–814 (1986)
Roemer, P.B., Edelstein, W.A., Hayes, C.E., Souza, S.P., Mueller, O.M.: The NMR phased array. Magn. Reson. Med. 16(2), 192–225 (1990)
Noll, D.C., Meyer, C.H., Pauly, J.M., Nishimura, D.G., Macovski, A.: A homogeneity correction method for magnetic resonance imaging with time-varying gradients. IEEE Trans. Med. Imaging 10(4), 629–637 (1991)
Hayes, C.E., Hattes, N., Roemer, P.B.: Volume imaging with MR phased arrays. Magn. Reson. Med. 18(2), 309–319 (1991)
Li, C., et al.: A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans. Image Process. 20(7), 2007–2016 (2011)
Zhang, K., Liu, Q., Song, H., Li, X.: A variational approach to simultaneous image segmentation and bias correction. IEEE Trans. Cybern. 45, 1426–1437 (2014)
Theodoridis, S., Koutroumbas, K.: Pattern Recognition. Academic, New York (2003)
Arms XRay.png: borrowed (NIH). http://rsbweb.nih.gov/ij/index.html
Brain MRI.png: borrowed. http://www.itk.org/(Kitware)
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Larbi, M., Messali, Z., Fortaki, T., Bouridane, A. (2019). A Novel Segmentation Algorithm Based on Level Set Approach with Intensity Inhomogeneity: Application to Medical Images. In: Chadli, M., Bououden, S., Ziani, S., Zelinka, I. (eds) Advanced Control Engineering Methods in Electrical Engineering Systems. ICEECA 2017. Lecture Notes in Electrical Engineering, vol 522. Springer, Cham. https://doi.org/10.1007/978-3-319-97816-1_33
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DOI: https://doi.org/10.1007/978-3-319-97816-1_33
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