Image study summarization of MR brain images by automated localization of relevant structures.
The paper discusses a methodology to objectify the patient presenting condition by automated selection of relevant images from a serial MR study. Structured data entry is used to capture the patient's chief complaint, pertinent history, signs, and symptoms. Expert created rules use this data to arrive at a differential and to identify the affected brain region/structure. Another expert created knowledge base then maps this information to the relevant image type, including image sequence specifics and orientation. A DICOM study reader identifies the relevant imaging sequences from the MR study. The structure localization method involves a search based on principal component analysis. A training set of subimages containing the structure of interest is used to generate a basis set of prototype images called eigenimages. The structure is located in an image by searching the image for a subregion that best matches the basis set. The structure localization was used to locate the lateral ventricles and orbits in nine images that were not part of the training set. The automated localizations were compared to expert localizations and the center of the regions located by the two techniques agreed to within +/- 1.7 mm (average for the nine localizations each of two structures).
Sinha
Usha
U
Department of Radiological Sciences, University of California at Los Angeles, 90095-1721, USA. usinha@itmedicine.net
Kangarloo
Hooshang
H
D001331:Automation; D001921:Brain / Q000033:anatomy & histology*; D001927:Brain Diseases / Q000175:diagnosis*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*