Medical image registration based on PCA and M_PSNR

Medical image registration is not only the premise of medical image fusion, but also the focus of medical image processing. It plays an important role in clinical diagnosis and treatment planning. Aiming at the problem of mutual information value in image registration, a PCA neural network and M_PSNR based method for rigid registration of reference and floating images was proposed. In this method, the centroid of the image needs to be figured out through the image matrix, and the rotation Angle and translation of the image registration can be calculated by PCA method to obtain the optimal rotation transformation and the initial registration value. Finally, M_PSNR method improved by PSNR was used for similarity test to increase the speed and efficiency of registration and simplify the calculation process

Exploiting multi-core and GPU hardware to speed up the registration of range images by means of Differential Evolution

Within this paper a general-purpose distributed evolutionary algorithm is presented, and is applied to the pair-wise registration of range images. Registration is carried out by utilizing the Grid Closest Point (GCP) for the graphical registration operations and the distributed algorithm to search for the best possible transformation of a scene image that, merged with the model image, yields a 3D reconstruction of the original object. The evolutionary algorithm is a distributed Differential Evolution algorithm that exploits an asynchronous migration mechanism and a multi-population recombination information exchange. Such an algorithm is provided with an adaptive updating scheme based on chaotic features for dynamically updating the control parameters. The scope of the paper is to speed up the registration process by using processor specialized to handle graphical operations and multi-core platforms. On the one hand, we investigate the use of either Graphic Processing Units (GPUs) or multi-core architectures to lower the execution time of the GCP procedure. On the other hand, we evaluate the performance of the distributed evolutionary algorithm in terms of solution quality by examining different multi-core architectures. Experimental results on a set of publicly available images show that, to perform the GCP, reductions in the execution times by one order of magnitude are obtained by harnessing the computational power of GPU and multi-core platforms with respect to the execution on a CPU-based framework. Furthermore, a comparison with the state-of-the-art sequential evolutionary algorithm for range image registration reveals that the adaptive distributed Differential Evolution algorithm allows attaining integral 3D models from 3D scan datasets that are better in terms of both quality and robustness