We present an energy-minimization-based framework for locating the centerline and estimating the width of tubelike objects from their structural network with a nonparametric model. The nonparametric representation promotes simple modeling of nested branches and n -way furcations, i.e., structures that abound in an arterial network, e.g., a cerebrovascular circulation. Our method is capable of extracting the entire vascular tree from an angiogram in a single execution with a proper initialization. A succinct initial model from the user with arterial network inlets, outlets, and branching points is sufficient for complex vasculature. The novel method is based upon the theory of principal curves. In this paper, theoretical extension to grayscale angiography is discussed, and an algorithm to find an arterial network as principal curves is also described. Quantitative validation on a number of simulated data sets, synthetic volumes of 19 BrainWeb vascular models, and 32 Rotterdam Coronary Artery volumes was conducted. We compared the algorithm to a state-of-the-art method and further tested it on two clinical data sets. Our algorithmic outputs-lumen centers and flow channel widths-are important to various medical and clinical applications, e.g., vasculature segmentation, registration and visualization, virtual angioscopy, and vascular atlas formation and population study.

Non-rigid image registration is widely used in medical image analysis and processing. We recently proposed a novel learning-based similarity measure for non-rigid image registration. The novel similarity measure is constructed by using two Kullback-Leibler distances (KLD), which are based on the a priori knowledge of the joint intensity distribution of a pre-aligned image pair. In this paper, we propose a new formulation for the novel KLD based similarity measure such that it can be exploited in Markov random field (MRF) based non-rigid registration framework with the graph-cuts algorithm. We have compared the proposed formulation against two other similarity measures under the same MRF-based framework, and two state-of-the-art approaches. According to the experimental results, it is demonstrated that the proposed method can achieve high registration accuracy.

Non-rigid image registration plays an important role in medical image analysis. Recently, Tang and Chung proposed to model the non-rigid medical image registration problem as an energy minimization framework. The optimization was done by using the graph-cuts algorithm via alpha-expansions. However, the dissimilarity measure used in the energy function of this graph-cuts based method was restricted to the sum of absolute differences (SAD) and the sum of squared differences (SSD). In this paper, to utilize an advanced dissimilarity measure, such as mutual information (MI), we adopt an approximation of MI to the graph-cuts based method. Exploiting the mutual information is valuable as it can capture complex statistical relationships between the intensities of the image pair without a priori knowledge of those relationships. We have compared the proposed method against the original graph-cuts based methods, and two state-of-the-art approaches. The experimental results demonstrate that the proposed method can achieve lower registration errors.

Non-rigid image registration is widely used in medical image analysis and image processing. It remains a challenging research problem due to its smoothness requirement and high degree of freedoms in the deformation process. A method is proposed to solve non-rigid image registration via graph-cuts algorithm by modeling the registration process as a discrete labeling problem. A displacement label (vector) is assigned to each pixel in the source image to indicate the corresponding position in the floating image. The whole system is then optimized by using the graph-cuts algorithm via alpha-expansions. As the initial point is not required for the graph-cuts algorithm, the method proposed is a single-level registration. In this paper, rather single-level, we enable multi-level non-rigid image registration using graph-cuts by passing the deformation field of the current resolution level to the successive finer one. By applying the proposed multi-level registration method, the number of labels used in each level is greatly reduced due to lower image resolution being used in coarser levels. Therefore, the speed of the registration process is improved. We compare our results with the original single-level version, DEMONS and FFD. It is found that our method improves the speed of non-rigid image registration by 50% and meanwhile maintains similar robustness and registration accuracy with the single-level version.