Nonrigid image registration of brain MR images using normalized mutual information

Abstract

Registration is an advanced technique which maps two images spatially and can produce an informative image. Intensity-based similarity measures are increasingly used for medical image registration that helps clinicians for faster and more effective diagnosis. Recently, mutual information (MI)-based image registration techniques have become popular for multimodal brain images. In this chapter, normalized mutual information (NMI) method has been employed for brain MR image registration. Here, the intensity patterns are encoded through similarity measure technique. NMI is an entropy-based measure that is invariant to the overlapped regions of the two images. To take care of the deformations, transformation of the floating image is performed using B-spline method. NMI-based image registration is performed for similarity measure between the reference and floating image. Optimal evaluation of joint probability distribution of the two images is performed using parzen window interpolation method. The hierarchical approach to nonrigid registration based on NMI is presented in which the images are locally registered and nonrigidly interpolated. The proposed method for nonrigid registration is validated with both clinical and artificial brain MR images. The obtained results show that the images could be successfully registered with 95% of correctness.