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Directly reading 228-layer 0.75-mm-thick consecutive CT Dicom format images of human body thoracolumbar into the Mimics, and then the three-dimensional geometric surface mesh model was acquired after the redundancy data was cleaned. Then the surface mesh model could be introduced into Ansys, and volumetric mesh in Ansys. Then the volumetric mesh was introduced into mimics to assign material, and the three-dimension finite element model was generated in Ansys. The triangles with low quality could be repaired in the mimics. This method is rapid and high accuracy. The three-dimension finite element model is very helpful to study biomechanics. [ABSTRACT FROM AUTHOR]

Secondary involvement of the colon by other malignant neoplasms may mimic primary colorectal carcinoma and results from direct extension, peritoneal seeding, or hematogenous spread. Careful gross examination and key histologic features may suggest the presence of a metastasis. Most primary tumors are solitary mucosa-based lesions, whereas secondary neoplasms may be multiple and often substantially affect the outer colonic wall. Primary colorectal cancers are comprised of malignant cribriform glands with necrosis and show dysplasia in the overlying mucosa, whereas secondary tumors show variable growth patterns and readily detectable lymphovascular invasion. Several benign processes, namely diverticulitis and intestinal endometriosis, may also mimic colon cancers and their nature may not be apparent prior to surgical intervention. [ABSTRACT FROM AUTHOR]

While most of existing methods use facial intensity images, a newest ones focus on introducing depth information to surmount some of classical face recognition problems such as pose, illumination, and facial expression variations. This abstract summarizes a new face recognition approach invariant to facial expressions based on dimensional surface matching. The core of our recognition/authentication scheme consists of aligning then comparing a probe face surface and gallery facial surfaces. In the off-line phase, we build the 3D face database with neutral expressions. The models inside include both shape and texture channels. In the on-line phase, a partial probe model is captured and compared either to all 3D faces in the gallery for identification scenario or compared to the genuine model for authentication scenario. The first step aligns probe and gallery models based only on static regions of faces within a new variant of the well known Iterative Closest Point called on R-ICP (Region-based Iterative Closest Point) which approximates the rigid transformations between the presented probe face and gallery one. R-ICP result is two matched sets of vertices in the both static and mimic regions of the face surfaces. For the second step, two geodesic maps are computed for the pair of vertices in the matched face regions. The recognition and authentication similarity score is based on the distance between these maps. Our evaluation experiments are done on 3D face dataset of IV2 french project. [ABSTRACT FROM AUTHOR]