Atlas-to-image non-rigid registration by minimization of conditional local entropy.
In this paper an algorithm for atlas-to-image non-rigid registration based on regional entropy minimization is presented. Tissue class probabilities in the atlas are registered with the intensities in the target image. The novel aspect of the paper consists in using tissue class probability maps that include the three main regions (for the brain, white matter, gray matter and csf) and a further partitioning thereof. For example, gray matter is further subdivided into basal ganglia (each of them defining its own class) and the rest (of gray matter). This guarantees a regional entropy minimization instead of just a global one. In other words, the local labels in the atlas will be adjusted in order to obtain the best explanation for the intensity distribution in the corresponding subregion of the target image.
D'Agostino
Emiliano
E
Katholieke Universiteit Leuven, Faculties of Medicine and Engineering, Medical Imaging Center (Radiology - ESAT/PSI), University Hospital Gasthuisberg, Herestraat 49, B-3000 Leuven, Belgium. Emiliano.DAgostino@esat.kuleuven.be
Maes
Frederik
F
Vandermeulen
Dirk
D
Suetens
Paul
P
D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D019277:Entropy; D005260:Female; 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*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique