PROCEEDINGS ARTICLE | March 8, 2007
Proc. SPIE. 6512, Medical Imaging 2007: Image Processing
KEYWORDS: Statistical analysis, Tumors, Wavelets, Interference (communication), Feature extraction, Image registration, Medical imaging, Gaussian filters, Image filtering, Statistical modeling
Registration of medical images (intra- or multi-modality) is the first step before any analysis is performed.
The analysis includes treatment monitoring, diagnosis, volumetric measurements or classification to mention a
few. While pairwise registration, i.e., aligning a floating image to a fixed reference, is straightforward, it is not
immediately clear what cost measures could be exploited for the groupwise alignment of several images (possibly
multimodal) simultaneously. Recently however there has been increasing interest in this problem applied to atlas
construction, statistical shape modeling, or simply joint alignment of images to get a consistent correspondence
of voxels across all images based on a single cost measure.
The aim of this paper is twofold, a) propose a cost function - alpha mutual information computed using
entropic graphs that is a natural extension to Shannon mutual information for pairwise registration and b)
compare its performance with the pairwise registration of the image set. We show that this measure can be
reliably used to jointly align several images to a common reference. We also test its robustness by comparing
registration errors for the registration process repeated at varying noise levels.
In our experiments we used simulated data, applying different B-spline based geometric transformations to the
same image and adding independent filtered Gaussian noise to each image. Non-rigid registration was employed
with Thin Plate Splines(TPS) as the geometric interpolant.
