Methods of tissue classification in MRI brain images play a significant role in computational neuroanatomy, particularly in automated ROI-based volumetry. A well-known and very simple k-NN classifier is used here without the need for user input during the training process. The classifier is trained with the use of tissue probability maps which are available in selected digital atlases of brain. The influence of misalignement between images and the tissue probability maps on the classifier's efficiency is studied in this paper. Deformable registration is used here to align the images and maps. The classifier's efficiency is tested in an experiment with data obtained from standard Simulated Brain Database. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Schwarz, D., & Kasparek, T. (2007). Brain tissue classification with automated generation of training data improved by deformable registration. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4673 LNCS, pp. 301–308). Springer Verlag. https://doi.org/10.1007/978-3-540-74272-2_38
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