Automated Tissue Classification in MRI Brain Images With the Use of Deformable Registration

• Authors: SCHWARZ Daniel; KAŠPÁREK Tomáš
• Year of publication: 2007
• Type: Article in Proceedings
• Conference: Proceedings of 15th European Signal Processing Conference EUSIPCO 2007
• MU Faculty or unit: Faculty of Medicine
• Field: Use of computers, robotics and its application
• Keywords: MRI;registration;classification;segmentation
• Description: 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 learning process. The classifier is trained with the use of tissue probabilistic maps which are available in selected digital atlases of brain. The influence of misalignement between images and the tissue probabilistic 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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