Comparing pairwise and simultaneous joint registrations of decorrelating interval exams using entropic graphs

Authors:
B. Ma;R. Narayanan;H. Park;A. O. Hero;P. H. Bland;C. R. Meyer
Affiliations:
Department of Radiology, University of Michigan, MI;Department of Biomedical Engineering, University of Michigan, MI;Department of Radiology, University of Michigan, MI;Department of Biomedical Engineering, University of Michigan, MI and Department of Electrical Engineering and Computer Science, University of Michigan, MI and Department of Statistics, University ...;Department of Radiology, University of Michigan, MI;Department of Radiology, University of Michigan, MI and Department of Biomedical Engineering, University of Michigan, MI
Venue:
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Year:
2007

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Abstract

The interest in registering a set of images has quickly risen in the field of medical image analysis. Mutual information (MI) based methods are well-established for pairwise registration but their extension to higher dimensions (multiple images) has encountered practical implementation difficulties. We extend the use of alpha mutual information (αMI) as the similarity measure to simultaneously register multiple images. αMI of a set of images can be directly estimated using entropic graphs spanning feature vectors extracted from the images, which is demonstrated to be practically feasible for joint registration. In this paper we are specifically interested in monitoring malignant tumor changes using simultaneous registration of multiple interval MR or CT scans. Tumor scans are typically a decorrelating sequence due to the cycles of heterogeneous cell death and growth. The accuracy of joint and pairwise registration using entropic graph methods is evaluated by registering several sets of interval exams. We show that for the parameters we investigated simultaneous joint registration method yields lower average registration errors compared to pairwise. Different degrees of decorrelation in the serial scans are studied and registration performance suggests that an appropriate scanning interval can be determined for efficiently monitoring lesion changes. Different levels of observation noise are added to the image sequences and the experimental results show that entropic graph based methods are robust and can be used reliably for multiple image registration.