Non-rigid registration of 2-D/3-D dynamic data with feature alignment

In this work, we are computing the matching between 2D manifolds and 3D manifolds with temporal constraints, that is we are computing the matching among a time sequence of 2D/3D manifolds. It is solved by mapping all the manifolds to a common domain, then build their matching by composing the forward mapping and the inverse mapping. At first, we solve the matching problem between 2D manifolds with temporal constraints by using mesh-based registration method. We propose a surface parameterization method to compute the mapping between the 2D manifold and the common 2D planar domain. We can compute the matching among the time sequence of deforming geometry data through this common domain. Compared with previous work, our method is independent of the quality of mesh elements and more efficient for the time sequence data. Then we develop a global intensity-based registration method to solve the matching problem between 3D manifolds with temporal constraints. Our method is based on a 4D(3D+T) free-from B-spline deformation model which has both spatial and temporal smoothness. Compared with previous 4D image registration techniques, our method avoids some local minimum. Thus it can be solved faster and achieve better accuracy of landmark point predication. We demonstrate the efficiency of these works on the real applications. The first one is applied to the dynamic face registering and texture mapping. The second one is applied to lung tumor motion tracking in the medical image analysis. In our future work, we are developing more efficient mesh-based 4D registration method. It can be applied to tumor motion estimation and tracking, which can be used to calculate the read dose delivered to the lung and surrounding tissues. Thus this can support the online treatment of lung cancer radiotherapy

Registration of the endoluminal surfaces of the colon derived from prone and supine CT colonography

Purpose: Computed tomographic (CT) colonography is a relatively new technique for detecting bowel cancer or potentially precancerous polyps. CT scanning is combined with three-dimensional (3D) image reconstruction to produce a virtual endoluminal representation similar to optical colonoscopy. Because retained fluid and stool can mimic pathology, CT data are acquired with the bowel cleansed and insufflated with gas and patient in both prone and supine positions. Radiologists then match visually endoluminal locations between the two acquisitions in order to determine whether apparent pathology is real or not. This process is hindered by the fact that the colon, essentially a long tube, can undergo considerable deformation between acquisitions. The authors present a novel approach to automatically establish spatial correspondence between prone and supine endoluminal colonic surfaces after surface parameterization, even in the case of local colon collapse.Methods: The complexity of the registration task was reduced from a 3D to a 2D problem by mapping the surfaces extracted from prone and supine CT colonography onto a cylindrical parameterization. A nonrigid cylindrical registration was then performed to align the full colonic surfaces. The curvature information from the original 3D surfaces was used to determine correspondence. The method can also be applied to cases with regions of local colonic collapse by ignoring the collapsed regions during the registration.Results: Using a development set, suitable parameters were found to constrain the cylindrical registration method. Then, the same registration parameters were applied to a different set of 13 validation cases, consisting of 8 fully distended cases and 5 cases exhibiting multiple colonic collapses. All polyps present were well aligned, with a mean (+/- std. dev.) registration error of 5.7 (+/- 3.4) mm. An additional set of 1175 reference points on haustral folds spread over the full endoluminal colon surfaces resulted in an error of 7.7 (+/- 7.4) mm. Here, 82% of folds were aligned correctly after registration with a further 15% misregistered by just onefold.Conclusions: The proposed method reduces the 3D registration task to a cylindrical registration representing the endoluminal surface of the colon. Our algorithm uses surface curvature information as a similarity measure to drive registration to compensate for the large colorectal deformations that occur between prone and supine data acquisitions. The method has the potential to both enhance polyp detection and decrease the radiologist's interpretation time. (C) 2011 American Association of Physicists in Medicine. [DOI: 10.1118/1.3577603

3D mesh metamorphosis from spherical parameterization for conceptual design

Engineering product design is an information intensive decision-making process that consists of several phases including design specification definition, design concepts generation, detailed design and analysis, and manufacturing. Usually, generating geometry models for visualization is a big challenge for early stage conceptual design. Complexity of existing computer aided design packages constrains participation of people with various backgrounds in the design process. In addition, many design processes do not take advantage of the rich amount of legacy information available for new concepts creation. The research presented here explores the use of advanced graphical techniques to quickly and efficiently merge legacy information with new design concepts to rapidly create new conceptual product designs. 3D mesh metamorphosis framework 3DMeshMorpher was created to construct new models by navigating in a shape-space of registered design models. The framework is composed of: i) a fast spherical parameterization method to map a geometric model (genus-0) onto a unit sphere; ii) a geometric feature identification and picking technique based on 3D skeleton extraction; and iii) a LOD controllable 3D remeshing scheme with spherical mesh subdivision based on the developedspherical parameterization. This efficient software framework enables designers to create numerous geometric concepts in real time with a simple graphical user interface. The spherical parameterization method is focused on closed genus-zero meshes. It is based upon barycentric coordinates with convex boundary. Unlike most existing similar approaches which deal with each vertex in the mesh equally, the method developed in this research focuses primarily on resolving overlapping areas, which helps speed the parameterization process. The algorithm starts by normalizing the source mesh onto a unit sphere and followed by some initial relaxation via Gauss-Seidel iterations. Due to its emphasis on solving only challenging overlapping regions, this parameterization process is much faster than existing spherical mapping methods. To ensure the correspondence of features from different models, we introduce a skeleton based feature identification and picking method for features alignment. Unlike traditional methods that align single point for each feature, this method can provide alignments for complete feature areas. This could help users to create more reasonable intermediate morphing results with preserved topological features. This skeleton featuring framework could potentially be extended to automatic features alignment for geometries with similar topologies. The skeleton extracted could also be applied for other applications such as skeleton-based animations. The 3D remeshing algorithm with spherical mesh subdivision is developed to generate a common connectivity for different mesh models. This method is derived from the concept of spherical mesh subdivision. The local recursive subdivision can be set to match the desired LOD (level of details) for source spherical mesh. Such LOD is controllable and this allows various outputs with different resolutions. Such recursive subdivision then follows by a triangular correction process which ensures valid triangulations for the remeshing. And the final mesh merging and reconstruction process produces the remeshing model with desired LOD specified from user. Usually the final merged model contains all the geometric details from each model with reasonable amount of vertices, unlike other existing methods that result in big amount of vertices in the merged model. Such multi-resolution outputs with controllable LOD could also be applied in various other computer graphics applications such as computer games

Structural Surface Mapping for Shape Analysis

Natural surfaces are usually associated with feature graphs, such as the cortical surface with anatomical atlas structure. Such a feature graph subdivides the whole surface into meaningful sub-regions. Existing brain mapping and registration methods did not integrate anatomical atlas structures. As a result, with existing brain mappings, it is difficult to visualize and compare the atlas structures. And also existing brain registration methods can not guarantee the best possible alignment of the cortical regions which can help computing more accurate shape similarity metrics for neurodegenerative disease analysis, e.g., Alzheimer’s disease (AD) classification. Also, not much attention has been paid to tackle surface parameterization and registration with graph constraints in a rigorous way which have many applications in graphics, e.g., surface and image morphing. This dissertation explores structural mappings for shape analysis of surfaces using the feature graphs as constraints. (1) First, we propose structural brain mapping which maps the brain cortical surface onto a planar convex domain using Tutte embedding of a novel atlas graph and harmonic map with atlas graph constraints to facilitate visualization and comparison between the atlas structures. (2) Next, we propose a novel brain registration technique based on an intrinsic atlas-constrained harmonic map which provides the best possible alignment of the cortical regions. (3) After that, the proposed brain registration technique has been applied to compute shape similarity metrics for AD classification. (4) Finally, we propose techniques to compute intrinsic graph-constrained parameterization and registration for general genus-0 surfaces which have been used in surface and image morphing applications