Intracranial deformation caused by brain tumors: assessment of 3-D surface by magnetic resonance imaging

Joint kinematics simulation from medical imaging data

A method for joint kinematics simulation is described. Kinematics parameters are determined from the relative displacement of marker sets placed on anatomical landmarks of surface models generated from medical imaging contour data. The landmarks are identified manually on fingers in multiple positions. A mathematical algorithm was then used to ascertain the kinematics axes of motion of the fingers. Once these axes are located, they are used as the base of a real time interactive simulation of the finger. The entire simulation was accomplished in a high-resolution graphics environment. A full complement of interactive tools (virtual dials and buttons controlled via mouse) was used to enhance the user interface. The development of the system, the model and the advantages and disadvantages of the method are discussed.

Knowledge-based interpretation of MR brain images

The authors have developed a method for fully automated segmentation and labeling of 17 neuroanatomic structures such as thalamus, caudate nucleus, ventricular system, etc. in magnetic resonance (MR) brain images. The authors' method is based on a hypothesize-and-verify principle and uses a genetic algorithm (GA) optimization technique to generate and evaluate image interpretation hypotheses in a feedback loop. The authors' method was trained in 20 individual T1-weighted MR images. Observer-defined contours of neuroanatomic structures were used as a priori knowledge. The method's performance was validated in eight MR images by comparison to observer-defined independent standards. The GA-based image interpretation method correctly interpreted neuroanatomic structures in all images from the test set. Computer-identified and observer-defined neuroanatomic structure areas correlated very well (r=0.99, y=0,95x-2.1). Border positioning errors were small, with a root mean square (rms) border positioning error of 1.5+/-0.6 pixels. The authors' GA-based image interpretation method represents a novel approach to image interpretation and has been shown to produce accurate labeling of neuroanatomic structures in a set of MR brain images.