Statistical shape modeling using MDL incorporating shape, appearance, and expert knowledge.
We propose a highly automated approach to the point correspondence problem for anatomical shapes in medical images. Manual landmarking is performed on a small subset of the shapes in the study, and a machine learning approach is used to elucidate the characteristic shape and appearance features at each landmark. A classifier trained using these features defines a cost function that drives key landmarks to anatomically meaningful locations after MDL-based correspondence establishment. Results are shown for artificial examples as well as real data.
Ward
Aaron D
AD
Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Canada. award@cs.sfu.ca
Hamarneh
Ghassan
G
D000465:Algorithms; D003198:Computer Simulation; D003337:Corpus Callosum / Q000033:anatomy & histology*; D005103:Expert Systems; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity