Brain Tissue Classification with Automated Generation of Training Data Improved by Deformable Registration

• Authors: SCHWARZ Daniel; KAŠPÁREK Tomáš
• Year of publication: 2007
• Type: Article in Proceedings
• Conference: LECTURE NOTES IN COMPUTER SCIENCE
• MU Faculty or unit: Faculty of Medicine
• Field: Use of computers, robotics and its application
• Keywords: image analysis;image registration;MRI;computational neuroanatomy;brain tissue classification;atlas-based 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 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.

Related projects

• The internal organisation and neurobiological mechanisms of functional CNS systems under normal and pathological conditions.
• Nonlinear multimodal registration for automatic morphometry of MRI brain images based on anatomically constrained spatial deformations