Kaoru Watanabe

On a new edge coloring related to multihop wireless networks

Multihop wireless networks consist of mobile terminals with personal communication devices. Each terminal can receive a message from a terminal and send it to the other terminal. In this paper, we discuss a new edge coloring problem in terms of graph and network theory on multihop wireless networks. This edge coloring problem takes the degree of interference into consideration. Then, we discuss this problem in terms of computational complexity.

Experimental results of 2D depth-depth matching algorithm based on depth camera Kinect v1

In the last year, we proposed a smart transcription algorithm. In the algorithm, a real liver is always captured by 3D depth camera. As contrasted with this, its virtual liver is represented by a polyhedron with STL (Standard Triangulated Language) format (Stereo-lithography) via DICOM (Digital Imaging and Communication in Medicine) data captured by MRI (Magnetic Resonance Imaging) and/or CT (Computed Tomography) scanner. By comparing a depth image in a real world and the Z-buffer in its virtual world, we quickly identify translation/rotation differences between real and virtual livers in GPU (Graphics Processing Unit). Then by a randomized steepest descent method based on the differences, we can quickly copy real lever motion to virtual liver motion. In this paper, this performance (motion precision and calculation time) of the proposed algorithm is ascertained in several kinds of experiments based on the depth camera Kinect v1. This is the first challenge to use our smart algorithm running in a 3D real environment.

Virtual Liver Surgical Simulator by Using Z-Buffer for Object Deformation

Virtual surgical simulator which is using computer graphics is much popular system than before. It is generally used in the medical areas, such as medical hospital or medical university. The simulator uses virtual organ models like liver, brain and so on. These models are usually based on the scanning data from patients and are used as volume models. Fortunately, the volume model is familiar with its cutting or deforming operation in a surgical system. For this reason, there are many kinds of surgical simulation or navigation systems using the volume model. However, visual reality of the volume model is not sufficient for human being including doctors. This means that the doctors cannot identify shape or location of a target organ from volume objects. In order to overcome this, we should use the translating method, such as marching cubes method and so on, for getting precisely polygon models which is included normal vectors of volume object. However, the method is quite time consuming and consequently the doctors cannot operate the virtual model in real-time.

Depth Camera Calibration and Knife Tip Position Estimation for Liver Surgery Support System

We have developed a liver surgery support system that uses two depth cameras and measures positional relationships between a surgical knife and a liver in real time. In this report, the overview of our system, the method for depth camera calibration, the estimation for knife tip positioning, and some experimental results are described.

A New Organ-Following Algorithm Based on Depth-Depth Matching and Simulated Annealing, and Its Experimental Evaluation

Medical simulation technology has been rapidly developing, owing to the production of cheap high-performance computers and depth cameras. We are developing a surgery navigation system. In such a system, it is important to track a virtual organ model based on a real organ model. For this purpose, we should examine the coincidence of positions/orientations between two organs at any time. In order to achieve this, we compare depth images of virtual or real organ model. The former is calculated by the z-buffer on a graphics processing unit (GPU) board, while the latter is captured by a depth camera. Then, we search for the same position/orientation between real and virtual organ models by comparing the differences between two depth images. In this paper, we use simulated annealing (SA) to search for the same position/orientation.

A CHANNEL ASSIGNMENT PROBLEM IN MULTIHOP WIRELESS NETWORKS AND GRAPH THEORY

Multihop wireless networks consist of mobile terminals with personal communication devices. Each terminal can receive a message and then send it to another terminal. In these networks, it is important to assign channels for communications to each terminal efficiently. There are some studies on this assignment problem using a conventional edge coloring in graph theory. In this paper, we propose a new edge coloring problem in graph and network theory on this assignment problem and we discuss the computational complexity of the problem. This edge coloring problem takes the degree of interference into consideration. Therefore, we can reuse the channels more efficiently compared with the conventional method.

Tracking a Real Liver Using a Virtual Liver and an Experimental Evaluation with Kinect v2

In this study, we propose a smart transcription algorithm for translation and/or rotation motions. This algorithm has two phases: calculating the differences between real and virtual 2D depth images, and searching the motion space defined by three translation and three rotation degrees of freedom based on the depth differences. One depth image is captured for a real liver using a Kinect v2 depth camera and another depth image is obtained for a virtual liver (a polyhedron in stereo-lithography (STL) format by z-buffering with a graphics processing unit). The STL data are converted from Digital Imaging and Communication in Medicine (DICOM) data, where the DICOM data are captured from a patient’s liver using magnetic resonance imaging and/or a computed tomography scanner. In this study, we evaluated the motion precision of our proposed algorithm based on several experiments based using a Kinect v2 depth camera.

Capturing a Surgical Area Using Multiple Depth Cameras Mounted on a Robotic Mechanical System

In our surgical navigation study, we construct a mechanical system for steadily capturing several surgical scenes by using two parallel robotic sliders and multiple vision cameras. In this paper, we first determine how to select an adequate time interval during which each camera projects a pattern to calculate depth against an organ. If multiple cameras project and receive patterns simultaneously, pattern interferences occur around the organ and, consequently, the cameras cannot capture depth images. Second, we investigate whether few or no occlusions occur in several surgical scenarios for an organ operation. Finally, we check experimentally whether distance precision in depth images is exactly maintained when a surgeon raises the camera to insert a microscope during a microsurgery. If the above functions are performed correctly, our proposed transcription algorithms for position, orientation, and shape from a real organ to its virtual polyhedron’s organ with STL-format play an active part during an actual surgery.

Depth Image Matching Algorithm for Deforming and Cutting a Virtual Liver via Its Real Liver Image Captured Using Kinect v2

In this paper, we propose a smart deforming and/or cutting transcription algorithm for rheology objects such as human livers. Moreover, evaluation of performance and shape precision under the proposed algorithm are experimentally verified by deforming a real clay liver and/or cutting a gel block prepared at human body temperature. First, we capture the image of the liver of a patient by digital imaging and communication in medicine (DICOM) generated by magnetic resonance imaging (MRI) and/or computed tomography (CT) scanner. Then, the DICOM data is segmented and converted into four types of stereo-lithography (STL) polyhedra, which correspond to the whole liver and three blood vessels. Second, we easily overlap the virtual and real liver images in our mixed reality (MR) surgical navigation system using our initial position/orientation/shape adjustment system that uses color images to differentiate between real and virtual depth images. After overlapping, as long as the real liver is deformed and/or cut by a human (doctor), the liver is constantly captured by Kinect v2. Subsequently, by using the real depth image captured in real time, many vertices around the virtual polyhedral liver in STL format are pushed/pulled by viscoelastic elements called the Kelvin–Voigt materials located on the vertices. Finally, after determining the displacements of the vertices, we obtain an adequately shaped STL. The vertex position required for fixing the shape is calculated using the Runge–Kutta method.

A new 2D depth-depth matching algorithm whose translation and rotation freedoms are separated

In this paper, we revise a previous 2D depth-depth-matching algorithm in order to copy motions fast from a real liver to a virtual liver in a surgical navigation. The real liver is always captured by 3D depth camera, and the virtual liver is represented by a polyhedron with STL format via DICOM captured by MRI/CT. In our algorithm, we firstly compare a 2D depth image in a real world and the Z-buffer of STL in a virtual world, and by using the difference of two depth images, we secondly search the best movement of a virtual liver from a huge number of possibilities with 3 translation and 3 rotation degrees-of-freedom. In this paper, we firstly divide translation and rotation D.O.F, and individually select the most adequate 3 DOF sets of a virtual liver following its real liver. Based on the division, we can find a sequence of following motions more precise and faster than our previous 2D depth-depth-matching algorithms.