The goal of nonrigid image registration is to find a suitable transformation such that the transformed moving image becomes similar to the reference image. The image registration problem can also be treated as an optimization problem, which tries to minimize an objective energy function that measures the differences between two involved images. In this paper, we consider image matching as the process of aligning object boundaries in two different images. The registration energy function can be defined based on the total energy associated with the object boundaries. The optimal transformation is obtained by finding the equilibrium state when the total energy is minimized, which indicates the object boundaries find their correspondences and stop deforming. We make an analogy between the above processes with the dislocation system in physics. The object boundaries are viewed as dislocations (line defects) in crystal. Then the well-developed dislocation energy is used to derive the energy assigned to object boundaries in images. The newly derived registration energy function takes the global gradient information of the entire image into consideration, and produces an orientation-dependent and long-range interaction between two images to drive the registration process. This property of interaction endows the new registration framework with both fast convergence rate and high registration accuracy. Moreover, the new energy function can be adapted to realize symmetric diffeomorphic transformation so as to ensure one-to-one matching between subjects. In this paper, the superiority of the new method is theoretically proven, experimentally tested and compared with the state-of-the-art SyN method. Experimental results with 3-D magnetic resonance brain images demonstrate that the proposed method outperforms the compared methods in terms of both registration accuracy and computation time.

Segmentation of deep brain structures is a challenging task for MRI images due to blurry structure boundaries, small object size and irregular shapes. In this paper, we present a new atlas-based segmentation method. It first uses a prior spatial dependency tree to constrain the relative positions between different deep brain structures and determine an optimal sequence for the structure by-structure segmentation. After positioning the structures, the segmentation result is further fine tuned by a non-rigid registration procedure between the atlas image and the target image using the histogram of the gradient magnitudes lying on the structure boundaries. The pro posed method has been applied on a publicly available MRI brain database and can achieve comparatively high segmentation accuracy.

Vessel enhancement is a primary preprocessing step for vessel segmentation and visualization of vasculatures. In this paper, a new vessel enhancement technique is proposed in order to produce accurate vesselness measures and vessel direction estimations that are less subject to local intensity abnormalities. The proposed method is called vessel enhancement using local line integrals and variational optimization (VE-LLI-VO). First, vessel enhancement using local line integrals (VE-LLI) is introduced in which a vessel model is embedded by regarding a vessel segment as a straight line based upon the second order information of the local line integrals. Useful quantities similar to the eigenvalues and eigenvectors of the Hessian matrix are produced. Moreover, based upon the local line integrals, junctions can be detected and handled effectively. This can help deal with the bifurcation suppression problem which exists in the Hessian-based enhancement methods. Then a more generic curve model is embedded to model vessels and a variational optimization (VO) framework is introduced to generate optimized vesselness measures. Experiments have been conducted on both synthetic images and retinal images. It is experimentally demonstrated that VE-LLI-VO produces improved performance as compared with the widely used techniques in terms of both vesselness measurement and vessel direction estimation.