An Automatic Medical Image Registration Approach Based on Common Sub-regions of Interest

Accurate and efficient image registration, based on interested common sub-regions is still a challenging task in medical image analysis. This paper presents an automatic features based approach for the rigid and deformable registration of medical images using interested common sub-regions. In the proposed approach, interested common sub-regions in two images (target image and source image) are automatically detected and locally registered. The final global registration is performed, using the transformation parameters obtained from the local registration. Registration using interested common sub-regions is always required in image guided surgery (IGS) and other medical procedures because it considers only the desired objects in medical images instead of the whole image contents. The proposed interested common sub-regions based registration is compared with the two states-of-the-art methods on MR images of human brain. In the experiments of rigid and deformable registrations, we show that our approach outperforms in terms of both the accuracy and time efficiency. The results reveal that interested common sub-region based registration can achieve good performance, regarding both the accuracy as well as the the time efficiency in monomodal brain image registration. In addition, the proposed approach also indicates the potential for multimodal images of different human organs.