Qingqi Hong

Implicit Reconstruction of Vasculatures Using Bivariate Piecewise Algebraic Splines

Vasculature geometry reconstruction from volumetric medical data is a crucial task in the development of computer guided minimally invasive vascular surgery systems. In this paper, a technique for reconstructing the geometry of vasculatures using bivariate implicit splines is developed. With the proposed technique, an implicit geometry representation of the vascular tree can be accurately constructed based on the voxels extracted directly from the surface of a certain vascular structure in a given volumetric medical dataset. Experimental results show that the geometric representation built using our method can faithfully represent the morphology and topology of vascular structures. In addition, both the qualitative and the quantitative validations have been performed to show that the reconstructed vessel geometry is of high accuracy and smoothness. An virtual angioscopy system has been implemented to indicate one of the strengths of our proposed method.

Topic evolution based on LDA and HMM and its application in stem cell research

This paper analyses topic segmentation based on the LDA (Latent Dirichlet Allocation) model, and performs the topic segmentation and topic evolution of stem cell research literatures in PubMed from 2001 to 2012 by combining the HMM (Hidden Markov Model) and co-occurrence theory. Stem cell research topics were obtained with LDA and expert judgements made on these topics to test the feasibility of the model classification. Further, the correlation between topics was analysed. HMM was used to predict the trend evolution of topics over various years, and a time series map was used to visualize the evolutional relationships among the stem cell topics.

3D vasculature segmentation using localized hybrid level-set method

BackgroundIntensity inhomogeneity occurs in many medical images, especially in vessel images. Overcoming the difficulty due to image inhomogeneity is crucial for the segmentation of vessel image.MethodsThis paper proposes a localized hybrid level-set method for the segmentation of 3D vessel image. The proposed method integrates both local region information and boundary information for vessel segmentation, which is essential for the accurate extraction of tiny vessel structures. The local intensity information is firstly embedded into a region-based contour model, and then incorporated into the level-set formulation of the geodesic active contour model. Compared with the preset global threshold based method, the use of automatically calculated local thresholds enables the extraction of the local image information, which is essential for the segmentation of vessel images.ResultsExperiments carried out on the segmentation of 3D vessel images demonstrate the strengths of using locally specified dynamic thresholds in our level-set method. Furthermore, both qualitative comparison and quantitative validations have been performed to evaluate the effectiveness of our proposed model.ConclusionsExperimental results and validations demonstrate that our proposed model can achieve more promising segmentation results than the original hybrid method does.

A survey on the visualization and reconstruction of vasculatures

Visualization and reconstruction of blood vessel from standard medical datasets play an important role in many clinical situations. This paper presents a survey on the visualization and reconstruction of vascular structures. Firstly, the visualization techniques of vasculatures are introduced, which includes volume rendering and surface rendering of vasculatures. Then, we focus on the reconstruction techniques of vascular structures, which can be classified into two categories: explicit reconstruction and implicit reconstruction of vascular structures. With reconstructed vascular geometry, it is quite easy to produce smooth visualization of vessel surfaces. In addition, finding the accurate geometric representation of vascular structures is crucial in developing computer aided vascular surgery systems.

An implicit skeleton-based method for the geometry reconstruction of vasculatures

Due to the high complexity of vascular system network, the geometry reconstruction of vasculatures from raw medical datasets remains a very challenging task. In this paper, we present a novel skeleton-based method for the geometry reconstruction of vascular structures from standard 3D medical datasets. With the proposed techniques, the geometry of vascular structures with high level of smoothness and accuracy can be reconstructed from the raw medical datasets. The experimental results and comparison with other techniques demonstrate that our method can achieve faithful and smooth vascular structures. In addition, quantitative validation has been conducted to evaluate the accuracy and smoothness of the reconstructed vessel geometry based on the proposed method.

Local Hybrid Level-set Method for MRA Image Segmentation

In this paper, a local hybrid level-set method for medical image segmentation is presented. In proposed method, a locally fitted binary energy function is introduced into the hybrid level-set framework proposed by Zhang et al.. Compared with the globally specified threshold, the use of local binary fitting energy in the hybrid level-set method allows one to extract local image information more accurately, which is essential for enhancing the efficiency and effectiveness of the segmentation of inhomogeneous images. Experimental results on 3D medical images are presented to demonstrate the strengths of the proposed method.

A skeleton-based technique for modelling implicit surfaces

In this paper, we develop a skeleton-based technique to model implicit surfaces using 2-D piecewise algebraic splines, which allows the construction of generalized cylinders with arbitrary cross-sections. Our method is based on smooth blending of a set of locally constructed general cylinders corresponding to different cross-sections along a given skeleton. Firstly, freeform cross-sections are reconstructed implicitly using the 2-D piecewise algebraic splines, and then, different cross-section profiles are weighted and summed up along the skeleton using the Partial Shape Preserving (PSP) spline basis functions. In addition, the smooth piecewise polynomial blending operations is employed to blend the branches of implicitly constructed generalized cylinders together. The implicit generalized cylinders constructed using our method is model free, and can achieve extremely high smoothness and accuracy.

Virtual angioscopy based on implicit vasculatures

Virtual endoscopy is among the most active areas in medical data visualization, which focuses on the simulated visualizations of specific hollow organs for the purposes of training and diagnosis. In this paper, we present a virtual angioscopy technique based on vasculature geometry reconstructed using skeleton-based implicit splines (SIS). The highly accurate implicit representation of the vasculature not only makes it possible to achieve high visual quality of perspective view inside the vessel structures, but also makes the implementation of an interactive virtual angioscopy a much easier task, as the issue of collision detection of virtual camera with vascular objects can be easily solved when the vasculature is represented in implicit form. Some experiments have been carried out to demonstrate the strengths of our technique.

The extraction of vascular axis based on signed distance function

This paper presents a simple and fast algorithm to extract the skeleton of vascular structures from segmented vessel datasets. Our algorithm is based on a step by step approach to move a small volume of interest along the vessel tree. With the introduction of Signed Distance Function (SDF), the moving sphere along the vessel tree can easily and automatically detect bifurcations and predict the location of next axis point. Some experiments have been carried out to demonstrate the strengths of our proposed method.

Towards additive manufacturing oriented geometric modeling using implicit functions

Surface-based geometric modeling has many advantages in terms of visualization and traditional subtractive manufacturing using computer-numerical-control cutting-machine tools. However, it is not an ideal solution for additive manufacturing because to digitally print a surface-represented geometric object using a certain additive manufacturing technology, the object has to be converted into a solid representation. However, converting a known surface-based geometric representation into a printable representation is essentially a redesign process, and this is especially the case, when its interior material structure needs to be considered. To specify a 3D geometric object that is ready to be digitally manufactured, its representation has to be in a certain volumetric form. In this research, we show how some of the difficulties experienced in additive manufacturing can be easily solved by using implicitly represented geometric objects. Like surface-based geometric representation is subtractive manufacturing-friendly, implicitly described geometric objects are additive manufacturing-friendly: implicit shapes are 3D printing ready. The implicit geometric representation allows to combine a geometric shape, material colors, an interior material structure, and other required attributes in one single description as a set of implicit functions, and no conversion is needed. In addition, as implicit objects are typically specified procedurally, very little data is used in their specifications, which makes them particularly useful for design and visualization with modern cloud-based mobile devices, which usually do not have very big storage spaces. Finally, implicit modeling is a design procedure that is parallel computing-friendly, as the design of a complex geometric object can be divided into a set of simple shape-designing tasks, owing to the availability of shape-preserving implicit blending operations.