Marching cubes: A high resolution 3D surface construction algorithm
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Surface reconstruction from unorganized points
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Active shape models—their training and application
Computer Vision and Image Understanding
Surface approximation of a cloud of 3D points
Graphical Models and Image Processing
Modelling with implicit surfaces that interpolate
ACM Transactions on Graphics (TOG)
Shape Priors for Level Set Representations
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Efficient Semiautomatic Segmentation of 3D Objects in Medical Images
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
Shape-Based Approach to Robust Image Segmentation using Kernel PCA
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Liver Surgery Planning Using Virtual Reality
IEEE Computer Graphics and Applications
Efficient kernel density estimation of shape and intensity priors for level set segmentation
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Automatic liver segmentation of contrast enhanced CT images based on histogram processing
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Active Scheduling of Organ Detection and Segmentation in Whole-Body Medical Images
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Liver Segmentation Using Automatically Defined Patient Specific B-Spline Surface Models
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Atlas-Based Automated Segmentation of Spleen and Liver Using Adaptive Enhancement Estimation
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
3D segmentation of the liver using free-form deformation based on boosting and deformation gradients
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Deformable segmentation via sparse shape representation
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
An automatic liver segmentation algorithm based on grow cut and level sets
Pattern Recognition and Image Analysis
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Volume segmentation is a relatively slow process and, in certain circumstances, the enormous amount of prior knowledge available is underused. Model-based liver segmentation suffers from the large shape variability of this organ, and from structures of similar appearance that juxtapose the liver. The technique presented in this paper is devoted to combine a statistical analysis of the data with a reconstruction model from sparse information: only the most reliable information in the image is used, and the rest of the liver's shape is inferred from the model and the sparse observation. The resulting process is more efficient than standard segmentation since most of the workload is concentrated on the critical points, but also more robust, since the interpolated volume is consistent with the prior knowledge statistics. The experimental results on liver datasets prove the sparse information model has the same potential as PCA, if not better, to represent the shape of the liver. Furthermore, the performance assessment from measurement statistics on the liver's volume, distance between reconstructed surfaces and ground truth, and inter-observer variability demonstrates the liver is efficiently segmented using sparse information.