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- W2030222393 abstract "Laser range scanners produce high resolution surface data of anatomic structures, which facilitates the determination of intraoperative soft tissue deformation and the performance of surface based image-to-physical space registration. Segmentation of the range scans is required for the data to be effectively incorporated into current image-guided procedures. Due to time constraints in the operating room, manual segmentation methods are not feasible. We propose a novel segmentation algorithm based on the level set method that uses information from the texture map and curvature of the acquired point cloud to provide an accurate edge map for computation of the speed image. Specifically, the edge image is created by combining the curvature values, computed from a surface fitted to the acquired point cloud using radial basis functions, and gradients of the RGB intensities in the texture map. Preliminary results, obtained from comparing the semiautomatic segmentations of intraoperatively acquire liver LRS data with manual gold standard segmentations, shows the method to be a significant first step towards the implementation of semiautomatic LRS segmentation routine during image-guided surgery." @default.
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- W2030222393 date "2005-04-29" @default.
- W2030222393 modified "2023-09-23" @default.
- W2030222393 title "Semiautomatic segmentation of textured laser range scans for use in image-guided procedures" @default.
- W2030222393 doi "https://doi.org/10.1117/12.595814" @default.
- W2030222393 hasPublicationYear "2005" @default.
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