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This paper describes a method for tracking the camera motion of a flexible endoscope, in particular a bronchoscope, using epipolar geometry analysis and intensity-based image registration. The method proposed here does not use a positional sensor attached to the endoscope. Instead, it tracks camera motion using real endoscopic (RE) video images obtained at the time of the procedure and X-ray CT images acquired before the endoscopic examination. A virtual endoscope system (VES) is used for generating virtual endoscopic (VE) images. The basic idea of this tracking method is to find the viewpoint and view direction of the VES that maximizes a similarity measure between the VE and RE images. To assist the parameter search process, camera motion is also computed directly from epipolar geometry analysis of the RE video images. The complete method consists of two steps: (a) rough estimation using epipolar geometry analysis and (b) precise estimation using intensity-based image registration. In the rough registration process, the method computes camera motion from optical flow patterns between two consecutive RE video image frames using epipolar geometry analysis. In the image registration stage, we search for the VES viewing parameters that generate the VE image that is most similar to the current RE image. The correlation coefficient and the mean square intensity difference are used for measuring image similarity. The result obtained in the rough estimation process is used for restricting the parameter search area. We applied the method to bronchoscopic video image data from three patients who had chest CT images. The method successfully tracked camera motion for about 600 consecutive frames in the best case. Visual inspection suggests that the tracking is sufficiently accurate for clinical use. Tracking results obtained by performing the method without the epipolar geometry analysis step were substantially worse. Although the method required about 20 s to process one frame, the results demonstrate the potential of image-based tracking for use in an endoscope navigation system.

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In this article, we describe the features of Virtual Bronchoscope System(VBS) and its practical use. VBS is constructed based on 3-D chest CT images. The bronchus region is automatically extracted from 3-D chest CT images by a three-dimensional region growing method. The surface rendering is employed for construction of virtualized tracheo-bronchial tree. It gives us an environment where we can observe inside the bronchi from an arbitrary viewpoint and a view direction. By mouse operation, the user can control the viewpoint and the view direction to fly through inside the airway in real time. VBS is applicable for a variety of purposes such as diagnosis, surgical planning, informed consent, education and training. One of extension of this system is a teaching tool for medical students. In the module for educational use, we have developed four functions for using the system as a teaching tool as follows: (a) automated display of bronchial anatomical names, (b) presenting questions about the currently observed branch in the endoscopic view, (c) display of the path which the user should follow, and (d) display of a question about the location of the artificially created tumor in the bronchus. These functions use the processed results of automated anatomical labeling. The method proposed here combines the knowledge based processing technique 'automated labeling of bronchial branch' and the novel visualization technique 'virtual bronchoscope'. This is one of new teaching tools of medical images. We conclude that this virtual bronchoscope system might have an important role in the medial students' education.

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This paper describes a method for the automated anatomical labeling of the bronchial branch extracted from a three-dimensional (3-D) chest X-ray CT image and its application to a virtual bronchoscopy system (VBS). Automated anatomical labeling is necessary for implementing an advanced computer-aided diagnosis system of 3-D medical images. This method performs the anatomical labeling of the bronchial branch using the knowledge base of the bronchial branch name. The knowledge base holds information on the bronchial branch as a set of rules for its anatomical labeling. A bronchus region is automatically extracted from a given 3-D CT image. A tree structure representing the essential structure of the extracted bronchus is recognized from the bronchus region. Anatomical labeling is performed by comparing this tree structure of the bronchus with the knowledge base. As an application, we implemented the function to automatically present the anatomical names of the branches that are shown in the currently rendered image in real time on the VBS. The result showed that the method could segment about 57% of the branches from CT images and extracted a tree structure of about 91% in branches in the segmented bronchus. The anatomical labeling method could assign the correct branch name to about 93% of the branches in the extracted tree structure. Anatomical names were appropriately displayed in the endoscopic view.

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The purpose of this study was to apply virtual reality technology to spiral computed tomographic (CT) angiogram images in a rabbit model of atherosclerosis and to correlate the images with histopathologic evaluation of the aorta. Image data were transferred to the virtual endoscope system in a graphics workstation. "Virtualized angioscopy" includes an interactive graphic user interface, which controls the viewpoint, the direction of the observation, and rendering and navigation functions. The virtual angioscopy system demonstrated irregularities of the luminal surface of the ascending aorta and a smooth luminal surface in the descending aorta. These observations were correlated with histopathologic findings. The results of this study indicate that the potential and real benefits of virtualized angioscopy far outweigh several technical limitations.

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In this paper we present a procedure which automatically extracts soft-tissue from multi-sliced head MRI images, and then displays three-dimensional shapes of the extracted soft-tissues. We use an iterative thresholding algorithm for segmentation in which an optimum threshold value is decided based on a goodness measure we proposed. This procedure can be used in a preliminary diagnosis in brain surgery without much effort of users, because of whole procedure including threshold selection and segmentation is performed automatically for rendering a three-dimensional image of soft-tissue's surface on a graphic terminal.

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A system for craniofacial surgical planning utilizing stacks of 2-D tomographic images is described. The four parts of the system are image generation, 2-D surgical planning, 3-D plan confirmation, and rough prediction of face shape after an operation. The four parts are combined to provide a useful surgical planning system. Because a gradient shading technique is used for generating 3-D images, the bumpy shape of the voxel (volume element for 3-D objects) sometimes obscures the essential shape of the displayed objects. To smooth undesirable bumps without losing the essential object shape, 2-D filtering is applied. Arbitrary bone blocks can be specified and moved interactively with a graphic terminal to any derived location to aid surgical planning. The chosen plan can be confirmed by observing computer-generated 3-D images from arbitrary directions since the process necessary to generate a 3-D image for any one direction should be adequate for all directions. Postoperative face-shape prediction is available for evaluating the operation plan at the last stage of the planning sequence.

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A practical simulation system based on analysis of clinical surgical procedures is described. The system provides fundamental manipulation operations to simulate actual surgical activities. There is a simulated cutting operation for sectioning a bone into various arbitrary shapes, a movement operation to transport a bone block to a desired position, and a restricted-movement operation to move a bone block until it comes into contact with other bone. The system also generates a skin surface image of a postoperative patient based on a simulated plan of bone manipulation. An operational system to enable physicians handling the system to work at ease has been devised. The structure and function of the system are described, and examples of its use in simulation are given.

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This paper describes a hip joint surgical simulation system using a three-dimensional image (X-ray CT images). We developed this system in cooperation with surgeons, and we have already applied it to several clinical examples. According to a surgeon who used our system for actual surgical plannings, most of the hip joint surgeries operated at the present time can be simulated. Our system has useful functions in order to plan surgeries such as interference check in movement of a bone and display of the attached surface of bone. We show an actual surgical planning process performed by the surgeon with our system.

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We developed a computer-aided interactive surgical simulation system for craniofacial anomalies based on three-dimensional (3-D) surface reconstruction CT imaging. This system has four functions: 1) 3-D surface reconstruction display with an accelerated projection method; 2) Surgical simulation to cut, move, rotate, and reverse bone-blocks over the reference 3-D image on the CRT screen; 3) 3-D display of the simulated image in arbitrary views; and 4) Prediction of postoperative skin surface features displayed as 3-D images in arbitrary views. Retrospective surgical simulation has been performed on three patients who underwent the fronto-orbital advancement procedures for brachycephaly and two who underwent the reconstructive procedure for scaphocephaly. The predicted configurations of the cranium and skin surface were well simulated when compared to the postoperative images in 3-D arbitrary views. In practical use, this software might be used for an on-line system connected to a large scale general-purpose computer.

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In this paper, we present a generalization of the distance transformation of a digitized picture in two different aspects. First, we define the generalized distance transformation of a binary picture (GDTB). A subclass of GDTB, called a local minimum filter family of GDTB (LMF-GDTB), characterized by a series of local minimum filters with varying neighborhoods, is discussed in detail. A skeleton is defined for LMF-GDTB, and it is proved that any binary picture can be reconstructed exactly from its skeleton with the distance value on it. Second, the gray weighted distance transformation (GWDT) is extended to a generalized GWDT (GGWDT) by introducing an arbitrary initial picture. After the fundamental equation of GGWDT and its solution are derived, it is proved that an arbitrary gray picture is generated by iterative application of GGWDT from a uniquely determined elementary picture and a sequence of initial value pictures.

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