Normalized mutual information based registration using k-means clustering and shading correction.
In this paper the influence of intensity clustering and shading correction on mutual information based image registration is studied. Instead of the generally used equidistant re-binning, we use k-means clustering in order to achieve a more natural binning of the intensity distribution. Secondly, image inhomogeneities occurring notably in MR images can have adverse effects on the registration. We use a shading correction method in order to reduce these effects. The method is validated on clinical MR, CT and PET images, as well as synthetic MR images. It is shown that by employing clustering with inhomogeneity correction the number of misregistrations is reduced without loss of accuracy thus increasing robustness as compared to the standard non-inhomogeneity corrected and equidistant binning based registration.
Knops
Z F
ZF
Utrecht University, Department of Computer Science, P.O. Box 80089, NL-3508 TB Utrecht, The Netherlands. zeger@cs.uu.nl
Maintz
J B A
JB
Viergever
M A
MA
Pluim
J P W
JP
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology; D016000:Cluster Analysis; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D007257:Information Theory; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique