Abstract
Magnetic Resonance Image segmentation is a fundamental task in a wide variety of computed-based medical applications that support therapy, diagnostic and medical applications. In this work, spatial information is included for estimating paramaters of a finite mixture model, with gaussian distribution assumption, using a modified version of the well-know Expectation Maximization algorithm proposed in [3]. Our approach is based on aggregating a transition step between E-step and M-step, that includes the information of spatial dependences between neighboring pixels.
Our proposal is compared with other approaches proposed in the image segmentation literature using the size and shape test, obtaining accurate and robust results in the presence of noise.
This work was supported by Research Grant Basal FB0821, “Centro Científico-Tecnológico de Valparaíso”, UTFSM.
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Gering, D., Nabavi, A., Kikinis, R., Hata, N., O’Donnell, L., Grimson, W., Jolesz, F., Black, P., Wells, W.: An Integrated Visualization System for Surgical Planning and Guidance Using Image Fusion and an Open MR. Journal of Magnetic Resonance Imaging 13, 967–975 (2001)
Ambroise, C., Govaert, G.: Spatial Clustering and the EM Algorithm, Tech. report, Université de technologie de Compiègne, France (1996)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. Journal of The Royal Statistical Society, Series B 39(1), 1–38 (1977)
Gil, P., Torres, F., Ortiz, F.G.: Detección de objetos por segmentación multinivel combinada de espacios de color, Tech. report, Dpto. Física, Ingeniería de Sistemas y Teoría de la Señal. Universidad de Alicate (Septiembre 2004)
Hu, T., Sung, S.Y.: Clustering Spatial Data with a Hybrid EM Approach, Tech. report, Department of Computer Science, National University of Singapore (2005)
Kaus, M.: Contributions to the Automated Segmentation of Brain Tumors in Magnetic Resonance Images, Ph.D. thesis, Der Technischen Fakultat der Universitat Erlangen-Nurnberg (1999)
Mostafa, M.G., Tolba, M.F., Gharib, T.F., Megeed, M.A.: Medical Image Segmentation Using a Wavelet-Based Multiresolution EM Algorithm. In: IEEE International Conference on Industrial Electronics, Technology and Automation, December 19-21 (2001)
Veloz, A., Chabert, S., Salas, R., Orellana, A., Vielma, J.: Fuzzy Spatial Growing for Glioblastoma Multiforme Segmentation on Brain Magnetic Resonance Imaging. In: Rueda, L., Mery, D., Kittler, J. (eds.) CIARP 2007. LNCS, vol. 4756, pp. 861–870. Springer, Heidelberg (2007)
Yang, Z., Chung, F.-L., Shitong, W.: Robust fuzzy clustering based image segmentation. Applied Soft Computing 9, 80–84 (2008)
Zhang, Y., Brady, M., Smith, S.: Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm. IEEE Transactions on Medical Imaging 20(1), 45–57 (2001)
Zhang, Y., Gerbrands, J.: Segmentation Evaluation Using Ultimate Measurament Accuracy. Image Processing Algorithms and Techniques III 1657, 449–460 (1996)
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Donoso, R., Veloz, A., Allende, H. (2010). Modified Expectation Maximization Algorithm for MRI Segmentation. In: Bloch, I., Cesar, R.M. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2010. Lecture Notes in Computer Science, vol 6419. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16687-7_13
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DOI: https://doi.org/10.1007/978-3-642-16687-7_13
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