A parametric deformable model to fit unstructured 3D data
Computer Vision and Image Understanding
Segmentation of Medical Image Objects Using Deformable Shape Loci
IPMI '97 Proceedings of the 15th International Conference on Information Processing in Medical Imaging
Enhanced Spatial Priors for Segmentation of Magnetic Resonance Imagery
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Reconstruction and Simplification of Surfaces from Contours
PG '99 Proceedings of the 7th Pacific Conference on Computer Graphics and Applications
Poisson surface reconstruction
SGP '06 Proceedings of the fourth Eurographics symposium on Geometry processing
IEEE Transactions on Information Technology in Biomedicine
Computer-aided kidney segmentation on abdominal CT images
IEEE Transactions on Information Technology in Biomedicine
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Organ segmentation and reconstruction are useful for many clinical purpose, like diagnostic aid or therapy planification. The segmentation literature is large. However, it is rare to find a method which performs both organ segmentation and reconstruction. Moreover, the major part of the literature focuses on the liver or lungs. We present a new method for kidney reconstruction from 3D CT scan. First, we perform a segmentation stage to extract the kidney volume from the greyscale image stack. Then, we refine this segmentation by analyzing the histogram of the kidney regions previously segmented. The refinement step eliminates the areas that was incorrectly considered as kidney region. A point cloud is extracted and is then reconstructed using the Poisson surface reconstruction. Thus, we obtain a 3D kidney model on which we apply a post-treatment in order to regularize it. Despite the reconstruction step, our method is fast and can be used real-time medical environment.