Abstract
We propose a deep study on tissue modelization and classification Techniques on T1-weighted MR images. Three approaches have been taken into account to perform this validation study. Two of them are based on Finite Gaussian Mixture (FGM) model. The first one consists only in pure Gaussian distributions (FGM-EM). The second one uses a different model for partial volume (PV) (FGM-GA). The third one is based on a Hidden Markov Random Field (HMRF) model. All methods have been tested on a Digital Brain Phantom image considered as the ground truth. Noise and intensity non-uniformities have been added to simulate real image conditions. Also the effect of an anisotropic filter is considered. Results demonstrate that methods relying in both intensity and spatial information are in general more robust to noise and inho-mogeneities. However, in some cases there is no significant differences between all presented methods.
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© 2002 Springer-Verlag Berlin Heidelberg
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Bach Cuadra, M., Platel, B., Solanas, E., Butz, T., Thiran, JP. (2002). Validation of Tissue Modelization and Classification Techniques in T1-Weighted MR Brain Images. In: Dohi, T., Kikinis, R. (eds) Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002. MICCAI 2002. Lecture Notes in Computer Science, vol 2488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45786-0_36
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DOI: https://doi.org/10.1007/3-540-45786-0_36
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