A Dense Point-to-Point Alignment Method for Realistic 3D Face Morphing and Animation

We present a new point matching method to overcome the dense point-to-point alignment of scanned 3D faces. Instead of using the rigid spatial transformation in the traditional iterative closest point (ICP) algorithm, we adopt the thin plate spline (TPS) transformation to model the deformation of different 3D faces. Because TPS is a non-rigid transformation with good smooth property, it is suitable for formulating the complex variety of human facial morphology. A closest point searching algorithm is proposed to keep one-to-one mapping, and to get good efficiency the point matching method is accelerated by a KD-tree method. Having constructed the dense point-to-point correspondence of 3D faces, we create 3D face morphing and animation by key-frames interpolation and obtain realistic results. Comparing with ICP algorithm and the optical flow method, the presented point matching method can achieve good matching accuracy and stability. The experiment results have shown that our method is efficient for dense point objects registration

Blending liquids

We present a method for smoothly blending between existing liquid animations. We introduce a semi-automatic method for matching two existing liquid animations, which we use to create new fluid motion that plausibly interpolates the input. Our contributions include a new space-time non-rigid iterative closest point algorithm that incorporates user guidance, a subsampling technique for efficient registration of meshes with millions of vertices, and a fast surface extraction algorithm that produces 3D triangle meshes from a 4D space-time surface. Our technique can be used to instantly create hundreds of new simulations, or to interactively explore complex parameter spaces. Our method is guaranteed to produce output that does not deviate from the input animations, and it generalizes to multiple dimensions. Because our method runs at interactive rates after the initial precomputation step, it has potential applications in games and training simulations

A novel flexible framework with automatic feature correspondence optimization for nonrigid registration in radiotherapy

Technical improvements in planning and dose delivery and in verification of patient positioning have substantially widened the therapeutic window for radiation treatment of cancer. However, changes in patient anatomy during the treatment limit the exploitation of these new techniques. To further improve radiation treatments, anatomical changes need to be modeled and accounted for. Non-rigid registration can be used for this purpose. This paper describes the design, the implementation and the validation of a new framework for non-rigid registration for radiotherapy applications. The core of this framework is an improved version of the Thin Plate Splines Robust Point Matching (TPS-RPM) algorithm. The TPS-RPM algorithm estimates a global correspondence and a transformation between the points that represent organs of interest belonging to two image sets. However, the algorithm does not allow for the inclusion of prior knowledge on the correspondence of subset of points and therefore, it can lead to inconsistent anatomical solutions. In this paper TPS-RPM was improved by employing a novel correspondence filter that supports simultaneous registration of multiple structures. The improved method allows for coherent organ registration and for the inclusion of user defined landmarks, lines and surfaces inside and outside of structures of interest. A procedure to generate control points form segmented organs is described. The framework parameters r and ?, which control the number of points and the non-rigidness of the transformation respectively, were optimized for three sites with different degrees of deformation: head and neck, prostate and cervix, using two cases per site. For the head and neck cases, the salivary glands were manually contoured on CT-scans, for the prostate cases the prostate and the vesicles, and for the cervix cases the cervix-uterus, the bladder and the rectum. The transformation error obtained using the best set of parameters was below 1 mm for all the studied cases. The length of the deformation vectors were on average (± 1 standard deviation) 5.8 ± 2.5 and 2.6 ± 1.1 mm for the head and neck cases, 7.2 ± 4.5 and 8.6 ± 1.9 mm for the prostate cases, and 19.0 ± 11.6 and 14.5 ± 9.3 mm for the cervix cases. Distinguishable anatomical features were identified for each case, and were used to validate the registration by calculating residual distances after transformation: 1.5 ± 0.8, 2.3 ± 1.0 and 6.3 ± 2.9 mm for the head and neck, prostate and cervix sites respectively. Finally, we demonstrated how the inclusion of these anatomical features in the registration process reduced the residual distances to 0.8 ± 0.5, 0.6 ± 0.5 and 1.3 ± 0.7 mm for the head and neck, prostate and cervix sites respectively. The inclusion of additional anatomical features produced more anatomically coherent transformations without compromising the transformation error. We concluded that the presented non-rigid registration framework is a powerful tool to simultaneously register multiple segmented organs with very different complexity

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus