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Abstract: geometric changes present a number of difficulties in deformable image registration. In this paper, we propose a global deformation framework to model geometric changes whilst promoting a smooth transformation between source and target images. To achieve this, we have developed an innovative model which significantly reduces the side effects of geometric changes in image registration, and thus improves the registration accuracy. Our key contribution is the introduction of a sparsity-inducing norm, which is typically L1 norm regularization targeting regions where geometric changes occur. This preserves the smoothness of global transformation by eliminating local transformation under different conditions. Numerical solutions are discussed and analyzed to guarantee the stability and fast convergence of our algorithm. To demonstrate the effectiveness and utility of this method, we evaluate it on both synthetic data and real data from traumatic brain injury (TBI). We show that the transformation estimated from our model is able to reconstruct the target image with lower instances of error than a standard elastic registration model.

The paper represents a novel algorithm for image registration with geometric changes. A sparse model is designed to provide different level of constraints on local deformations. Also, a new energy optimization scheme is introduced to preserve the topology and uniquely describe the correspondence between images, and a numerical dual technique is applied to speed up the convergence and enhance the stability. The authors show experimental results that suggest that their algorithm outperforms the traditional elastic image registration on the registration accuracy and processing time.

带有几何形变的变形图像配准

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