Nonrigid image registration using similarity measure based on spatially weighted global probability distribution function

In nonrigid image registration, similarity measures including spatial information have been shown to perform better than those measures without spatial information. In this work, we provide new insight to the relationships among regional mutual information, regional probability distribution functions (PDFs) and global PDFs, and propose a novel nonrigid registration scheme with spatially weighted global probability distribution function (SWGPDF). Similarity measures based on SWGPDF (SWGPDFSM) are constructed. Three different spatial sub-region division methods are compared: the equally spaced sub-region (ESSR), the local binary pattern sub-region (LBPSR) and the gradient sub-region (GSR). The registration scheme applies B-spline based free form deformations (FFDs) as the transformation model. A Parzen window and linear interpolation are used to construct histograms. The SWGPDFSM registration scheme with ESSR space division is compared with the traditional global mutual information (gMI), the traditional global normalized mutual information (gNMI), regional mutual information and the SWGPDFSM with LBPSR or GSR space division. The test results show that SWGPDFSM scheme with ESSR space division outperforms the other schemes for elastically aligning images in the presence of big geometrical transformations, bias fields and illumination changes.