A novel hybrid method for the registration of deformable medical images using common subregions

Deformable image registration is a fundamental technique in medical image analysis and provide physicians with a more complete understanding of patient anatomy and function. Deformable image registration has potentially improved the geometric precision of medical images with large and complex deformations. During the last decade, several types of deformable image registration techniques have been developed with high accuracy and efficiency. However, little work has been done regarding how image registration is performed based on interested common subregions which is still an open question in the field. In this paper, we propose a hybrid method that combines local rigid registration with global deformable registration to address the issue of interested common subregions registration. We first detected and segmented interested common subregions in source and target images, and then registered the obtained interested common subregions with local transformations. The obtained transformation parameters in local registration are then applied on the deformed source image which recovered it according to the coordinates of fixed target image. The fixed target image is then rigidly registered with the recovered source image. Finally, the obtained image from rigid image registration is again registered with fixed target image using deformable registration and a more informative registered image is obtained. The main objective of the proposed work is to address the issue of interested common subregions-based registration in deformable medical images. The proposed deformable image registration method is evaluated on 2D brain MRI images of glioma patients. The evaluation parameters are mean square error (MSE), sum of squared differences (SSD), peak signal to noise ratio (PSNR), cross-correlation CC) and computation time. The overall Analysis of variance (ANOVA) for the mentioned variables are significant (i.e., the value of P < 0.5) which shows the accuracy and efficiency of the proposed method compared to the existing methods.