Automated localization and identification of lower spinal anatomy in magnetic resonance images.
Clinical interpretation of the subtle changes present in MR images in the setting of disease currently relies on subjective image analysis. Image evaluation could potentially be improved by computerized segmentation and precise quantification of the image anatomy. However, this cannot be automated unless reliable navigation within an image is established, capable of compensating for unpredictable factors such as anatomical variability, positioning of an image plane in the body, and variable image characteristics. Focusing on the lower spinal region, this paper explores the presence of image- and anatomy-invariant features which facilitate automated, unconstrained identification, and localization of basic lower spine anatomy.
Chwialkowski
M P
MP
Department of Electrical Engineering, University of Texas, Arlington.
Shile
P E
PE
Pfeifer
D
D
Parkey
R W
RW
Peshock
R M
RM
D000465:Algorithms; D001330:Electronic Data Processing; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008954:Models, Biological; D012016:Reference Values; D015203:Reproducibility of Results; D013131:Spine / Q000033:anatomy & histology*