The aim of this study is to support medical experts to be able to make an order among large number of automatic registration. The experts could tackle with the most problematic cases due to the inaccuracy of automatic registration procedure in the vicinity of the bronchus to help virtual bronchoscopy (VB) systems. Functional images (e.g. PET) can be projected on the relevant part of the organ that is examined in VB systems. We collected cases where the difference between the time of low-dose (ldCT) and diagnostic CT (hdCT) was less than one year. Altogether 22 anonymous ldCT and hdCT studies were selected in this study. Based on the literature, a potential candidate for image registration was elastix. We applied a specific in-house developed application for image preprocessing, before the elastix registration. We tried to resolve the goodness of the entire registration process by visual judgment combining it with special numerical features. Numerical data include the mutual information, standardized mutual information, Kullback-Leibler entropy, cross correlation, L1norm, L2norm square. We found satisfying correlation between mutual information and visual judgment in the close vicintiy of airtree. We calculated confident intervals for MI of acceptable and rejected registrations about the mean values of it by bootstraping method: [0.27, 0.36], [0.13, 0.19].