Most of the three-dimensional (3-D) reconstructions of human organs rely on medical volume data. In this paper, we propose to use the endoscopic image sequences as a new image modality for surface reconstruction of human organs or tissues. Using the pinhole camera model, the original 3-D points are projected to two-dimensional (2-D) images by multiplying transformation matrices. We assume the intrinsic camera parameters, such as the focal length and principal points, are known and simplify the transformation matrices to only include the camera motion, i.e. camera rotation and translation. Using the factorization method for recovering the shape of the object and the motion of the camera from an image sequence, the 3-D structures are computed. 3-D reconstruction from endoscopic image sequences is a new exploration. It provides additional information that facilitates the understanding for the lesion areas three-dimensionally. And the reconstructed structures directly correspond to the original images and can be rendered with precise texture-mapping easily. It has potential to be used to guide surgery and serve as an alternative data source for constructing new stereo endoscopy systems using one camera.
We design a 3D facial surgical simulation system, which predicts the patient's post-surgical appearance from his CT volume data. The general steps adopted in this system include data acquisition, image preprocessing, 3D reconstruction, simulation, and visualization. In order to predict surgical results well, we adopt finite element modeling to estimate the simulation outcome. For surgical simulation, we utilize the isoparametric hexahedron finite element model to represent that facial structure manipulated during the surgical operation. Isoparametric hexahedron element is a more flexible and accurate element type than the tetrahedron one, which was used for surgical simulation in other literatures. Experimental results show that the proposed method is able to simulate facial surgery effectively.
Acute pyelonephritis is a serious disease in children that may result in irreversible renal scarring. The ability to localize the site of urinary tract infection and the extent of acute pyelonephritis has considerable clinical importance. In this paper, we are devoted to segment the acute pyelonephritis area from kidney SPECT images. A two-step algorithm is proposed. First, the original images are translated into binary versions by automatic thresholding. Then the acute pyelonephritis areas are located by finding convex deficiencies in the obtained binary images. This work gives important diagnosis information for physicians and improves the quality of medical care for children acute pyelonephritis disease.
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