A Markov random field model for bony tissue classification
3D biomedical images are a valuable source of information for clinical diagnosis. In areas such as bone remodeling, fracture prediction and prosthesis design, the external geometry of the bones needs to be precisely defined and injuries identified. A system that automatically interprets and presents a 3D reconstruction of the bone can be very useful, although this task cannot be carried out without specific knowledge of the domain. This knowledge may be represented by a set of constraints over properties and relationships between regions. In this work we present a Markov random field model for identification of injuries found in the proximal tibia.
markov random field gibbs distributions labeling computed tomography (ct) images rule-based system