Continuous image representations avoid the histogram binning problem in mutual information based image registration

Mutual information (MI) based image-registration methods that use histograms are known to suffer from the so-called binning problem, caused by the absence of a principled technique for choosing the "optimal" number of bins to calculate the joint or marginal distributions. In this paper, we show that foregoing the notion of an image as a set of discrete pixel locations, and adopting a continuous representation is the solution to this problem. A new technique to calculate joint image histograms is proposed, which makes use of such a continuous representation. We report results on affine registration of a pair of 2D medical images under high noise, and demonstrate the smoothness of various information-theoretic similarity measures such as joint entropy or MI w.r.t. the transformation, when our proposed technique (referred to as the "robust histogram") is adopted to compute the required probability distributions.

• Type: Conference
• Year: 2006
• Where: ISBI
• Authors: Ajit Rajwade, Arunava Banerjee, Anand Rangarajan
• Keywords: Information-theoretic Similarity Measures, ISBI 2006, Joint Image Histograms, Medical Imaging, Required Probability Distributions