We present a method for synthesizing two dimensional (2D)

Abstract:The Purkinje fibers are located in the ventricular walls of the heart, just beneath the endocardium and conduct excitation from the right and left bundle branches to the ventricular myocardium. Recently, anatomists succeeded in photographing the Purkinje fibers of a sheep, which clearly showed the mesh structure of the Purkinje fibers. In this study, we present a technique for modeling the mesh structure of Purkinje fibers semiautomatically using an extended L-system. The Lsystem is a formal grammar that defines the growth of a fractal structure by generating rules (or rewriting rules) and an initial structure. It was originally formulated to describe the growth of plant cells, and has subsequently been applied for various purposes in computer graphics such as modeling plants, buildings, streets, and ornaments. For our purpose, we extended the growth process of the L-system as follows: 1) each growing branch keeps away from existing branches as much as possible to create a uniform distribution, and 2) when branches collide, we connect the colliding branches to construct a closed mesh structure. We designed a generating rule based on observations of the photograph of Purkinje fibers and manually specified three terminal positions on a three-dimensional (3D) heart model: those of the right bundle branch, the anterior fascicle, and the left posterior fascicle of the left branch. Then, we grew fibers starting from each of the three positions based on the specified generating rule. We achieved to generate 3D models of Purkinje fibers of which physical appearances closely resembled the real photograph. The generation takes a few seconds. Variations of the Purkinje fibers could be constructed easily by modifying the generating rules and parameters.Key words: Purkinje fibers, L-system, heart simulation.A three-dimensional (3D) virtual heart model is often used for computer simulations and visualizations. Computer simulation is one way to understand the electrophysiological properties of the heart or to figure out the mechanisms of fatal arrhythmias [1][2][3][4]. Effective visualization of a 3D heart model is also a useful tool for education and communication between doctors and patients [1,5]. However, the creation of a 3D heart model is difficult and time-consuming because the heart has intricate structures containing various tissues, such as the atrioventricular node, bundle of His, Purkinje fi bers, and contractive myocardium. Our goal was to facilitate this process by providing effective modeling tools. In this study, we focused on the construction of Purkinje fi bers.The Purkinje fi bers are part of the ventricular conduction system and were originally discovered by Tawara [6]. These tissues conduct excitation (electrical activation) rapidly from the bundle of His to the ventricular myocardial tissue. The Purkinje fibers are located in the ventricular walls of the heart, just beneath the endocardium. Figure 1 is a PAS-stained stereomicrograph of a sheep heart provided by Shimada et al. [7], which shows the...

Abstract. L-system is a tool commonly used for modeling and simulating the growth of plants. In this paper, we propose a new tree modeling system based on L-system that allows the user to control the overall appearance and the depth of recursion, which represents the level of growth, easily and directly, by drawing a single stroke. We introduce a new module into L-system whose growth direction is determined by a user-drawn stroke. As the user draws the stroke, the system gradually advances the growth simulation and creates a tree model along the stroke. Our technique is the first attempt to control the growth of a simulation in L-system using stroke input.

We present an interactive modeling system for flower composition that supports seamless transformation from an initial sketch to a detailed three-dimensional (3D)

x a d c x b Figure 1: Eustoma model generated with our technique. From a CT volume (a) of a sample flower (b: photograph), we reconstructed a flower model (c). Pane (x) is a cross section of the volume (a). The reconstructed flower model was rendered with texture (d). AbstractThis paper presents a novel three dimensional (3D) flower modeling technique that utilizes an X-ray computed tomography (CT) system and real-world flowers. Although a CT system provides volume data that captures the internal structures of flowers, it is difficult to accurately segment them into regions of particular organs and model them as smooth surfaces because a flower consists of thin organs that contact one another. We thus introduce a semiautomatic modeling technique that is based on a new active contour model with energy functionals designed for flower CT. Our key idea is to approximate flower components by two important primitives, a shaft and a sheet. Based on our active contour model, we also provide novel user interfaces and a numerical scheme to fit these primitives so as to reconstruct realistic thin flower organs efficiently. To demonstrate the feasibility of our technique, we provide various flower models reconstructed from CT volumes.

Figure 1: Our segmentation example. We cut out the head region from the CT volume of a stuffed bear. From specified contours (a), we computed a scalar field (b) and generated a boundary from the field (c). We smoothed the right half of the volume and computed the boundaries (d) using a previous method (left) and our B-HRBF (right) with the same contours. AbstractIn this paper, we propose a novel contour-based volume image segmentation technique. Our technique is based on an implicit surface reconstruction strategy, whereby a signed scalar field is generated from user-specified contours.The key idea is to compute the scalar field in a joint spatial-range domain (i.e., bilateral domain) and resample its values on an image manifold. We introduce a new formulation of Hermite radial basis function (HRBF) interpolation to obtain the scalar field in the bilateral domain. In contrast to previous implicit methods, bilateral HRBF (B-HRBF) generates a segmentation boundary that passes through all contours, fits high-contrast image edges if they exist, and has a smooth shape in blurred areas of images. We also propose an acceleration scheme for computing B-HRBF to support a real-time and intuitive segmentation interface. In our experiments, we achieved high-quality segmentation results for regions of interest with high-contrast edges and blurred boundaries.

Medical volume images contain ambiguous and low-contrast boundaries around which existing fully-or semiautomatic segmentation algorithms often cause errors. In this paper, we propose a novel system for intuitively and efficiently refining medical volume segmentation by modifying multiple curved contours. Starting with segmentation data obtained using any existing algorithm, the user places a three-dimensional curved cross-section and contours of the foreground region by drawing a cut stroke, and then modifies the contours referring to the cross-section. The modified contours are used as constraints for deforming a boundary surface that envelops the foreground region, and the region is updated by that deformed boundary. Our surface deformation algorithm seamlessly integrates detail-preserving and curvature-diffusing methods to keep important detail boundary features intact while obtaining smooth surfaces around unimportant boundary regions. Our system supports topological manipulations as well as contour shape modifications. We illustrate the feasibility of our system by providing examples of its application to the extraction of bones, muscles, kidneys with blood vessels, and bowels. Figure 1. Refining the end of a thigh bone with our system. Starting with segmentation data containing errors (a), the user places and modifies multiple contour curves (b) to refine the segmented region with voxel-level accuracy (c). Our system provides a set of user interfaces for the placement of contour curves (d) and their modification (e).

In this paper, we present a three-dimensional (3D) digitization technique for natural objects, such as insects and plants. The key idea is to combine X-ray computed tomography (CT) and photographs to obtain both complicated 3D shapes and surface textures of target specimens. We measure a specimen by using an X-ray CT device and a digital camera to obtain a CT volumetric image (volume) and multiple photographs. We then reconstruct a 3D model by segmenting the CT volume and generate a texture by projecting the photographs onto the model. To achieve this reconstruction, we introduce a technique for estimating a camera position for each photograph. We also present techniques for merging multiple textures generated from multiple photographs and recovering missing texture areas caused by occlusion. We illustrate the feasibility of our 3D digitization technique by digitizing 3D textured models of insects and flowers. The combination of X-ray CT and a digital camera makes it possible to successfully digitize specimens with complicated 3D structures accurately and allows us to browse both surface colors and internal structures.