Abstract
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.
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Schwarz, D., Kasparek, T. (2007). Brain Tissue Classification with Automated Generation of Training Data Improved by Deformable Registration. In: Kropatsch, W.G., Kampel, M., Hanbury, A. (eds) Computer Analysis of Images and Patterns. CAIP 2007. Lecture Notes in Computer Science, vol 4673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74272-2_38
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DOI: https://doi.org/10.1007/978-3-540-74272-2_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74271-5
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