Hiroyuki Hishida

CT image segmentation using FEM with optimized boundary condition.

The authors propose a CT image segmentation method using structural analysis that is useful for objects with structural dynamic characteristics. Motivation of our research is from the area of genetic activity. In order to reveal the roles of genes, it is necessary to create mutant mice and measure differences among them by scanning their skeletons with an X-ray CT scanner. The CT image needs to be manually segmented into pieces of the bones. It is a very time consuming to manually segment many mutant mouse models in order to reveal the roles of genes. It is desirable to make this segmentation procedure automatic. Although numerous papers in the past have proposed segmentation techniques, no general segmentation method for skeletons of living creatures has been established. Against this background, the authors propose a segmentation method based on the concept of destruction analogy. To realize this concept, structural analysis is performed using the finite element method (FEM), as structurally weak areas can be expected to break under conditions of stress. The contribution of the method is its novelty, as no studies have so far used structural analysis for image segmentation. The methods implementation involves three steps. First, finite elements are created directly from the pixels of a CT image, and then candidates are also selected in areas where segmentation is thought to be appropriate. The second step involves destruction analogy to find a single candidate with high strain chosen as the segmentation target. The boundary conditions for FEM are also set automatically. Then, destruction analogy is implemented by replacing pixels with high strain as background ones, and this process is iterated until object is decomposed into two parts. Here, CT image segmentation is demonstrated using various types of CT imagery.

Flattening simulations of 3D thick sheets made of fiber composite materials

Abstract Recently, fiber composite materials have been attracting attention from industry because of their remarkable material characteristics, including light weight and high stiffness. However, the costs of products composed of fiber materials remain high because of the lack of effective manufacturing and designing technologies. To improve the relevant design technology, this paper proposes a novel simulation method for deforming fiber materials. Specifically, given a 3D model with constant thickness and known fiber orientation, the proposed method simulates the deformation of a model made of thick fiber-material. The method separates a 3D sheet model into two surfaces and then flattens these surfaces into two dimensional planes by a parameterization method with involves cross vector fields. The cross vector fields are generated by propagating the given fiber orientations specified at several important points on the 3D model. Integration of the cross vector fields gives parameterization with low-stretch and low-distortion.

CT image segmentation using structural analysis

We propose a segmentation method for blurred and low-resolution CT images focusing physical properties. The basic idea of our research is simple: two objects can be easily separated in areas of structural weakness. Given CT images of an object, we assign a physical property such as Youngs modulus to each voxel and create functional images (e.g., von Mises strain at the voxel). We then remove the voxel with the largest value in the functional image, and these steps are reiterated until the input model is decomposed into multiple parts. This simple and unique approach provides various advantages over conventional segmentation methods, including preciousness and noise robustness. This paper also demonstrates the efficiency of our approach using the results of various types of CT images, including biological representations and those of engineering objects.

3D woven composite design using a flattening simulation

Fiber composite materials have unique, advantageous mechanical properties that have made them highly desirable in a range of industries. In particular, 3D woven-fiber composites are highly resistant to delamination compared with laminated 2D woven-fiber composites and have been adopted in various advanced products. This paper focuses on the design of 3D woven-fiber composite products and proposes a flattening simulation method for designed 3D models with constant thickness. The proposed method estimates the shape of a flat material and the fiber directions in the 3D model design; deformation phenomena of 3D woven-fiber materials are also considered in order to improve the accuracy of the proposed method. CT images are used to compare the simulation results with the actual deformation of 3D woven-fiber materials and confirm the ability of our method to effectively design the fiber direction base on the 3D model and to estimate the shape of flat materials. Flattened shape of fiber material is obtained for given product design.Flattening simulation deal with the anisotropy and thickness of 3D woven fiber materials.Flatten shape is optimized for given fiber directions.

CT Image Segmentation for Bone Structures Using Image-Based FEM

We propose a CT image segmentation method using structural analysis. The aim of our research is to decompose assembled fossil of skeletons and crania into fragments in the area of fossil reconstruction. One challenge specific to this type of segmentation procedure is the separation of fragments where their gaps are not necessarily clear. We previously proposed a method of segmenting CT images using structural analysis. This technique is based on the assumption that the interference area (joint) between components (bones) is structurally weak. We compute strain, which tends to be large in structurally weak areas and segment the image in the region of high strain. With this approach, there is a need to specify boundary conditions for the structural analysis, namely, loading conditions (loading forces and their positions) and locations of fixed boundaries. In our previous work, we proposed a method of optimizing loading forces given loading positions and fixed boundary positions. In this study, we propose a method to find both of those positions to automate the segmentation procedure. Some segmentation results generated by our prototype software demonstrate applicability of the proposed method.

Fine-grained phenotypic analyses of motor functions for laboratory mice: The inverse kinematics of mouse gait patterns

P3-h07 Basis of Body-Mind Axis (I): Significance and specialty of trunk (soma) regulation in balance control of the standing human being, and its evaluation of abdomen muscle activities by ultrasound imaging Yoriko Atomi 1 , Tomoaki Atomi 2, Noboru Hirose 2, Miho Shimizu 3, Muneko Ishimizu 4 1 Radioisotope Center, The Univ. of Tokyo, Tokyo, Japan 2 Teikyo Sci. Univ., Uenohara, Japan 3 Grad. Sch. of Inf. Technol. Sci., The Univ. of Tokyo, Tokyo, Japan 4 Dept. of Arts and Sci., The Univ. of Tokyo, Tokyo, Japan