An efficient model-guided framework for alignment of brain MR image sequences

This paper proposes a method for alignment of human brain magnetic resonance (MR) image sequences in the brain based on a 3D human brain model (triangulated mesh). The brain model is composed of four components, namely, cerebrum, cerebellum, brain stem and pituitary gland which are represented by four different colors. Synthesized image sequences (cross-sections) are extracted from the model at regular intervals for sagittal and coronal views as done in MR imaging. The cerebellums are segmented from the sequence of MR images by using the method of active contouring and their sizes are determined. The areas of the cerebellums are computed from the cross-sections using the color information. To obtain the optimal synthesized cross-section sequence corresponding to the series of MR images, an efficient dynamic programming based computational technique has been developed that uses the normalized sizes of cerebellum in both the MR image sequences and the cross-sections.