Active shape models—their training and application
Computer Vision and Image Understanding
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
How To Deal with Point Correspondences and Tangential Velocities in the Level Set Framework
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
International Journal of Computer Vision
Joint Parametric and Non-parametric Curve Evolution for Medical Image Segmentation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Local Statistic Based Region Segmentation with Automatic Scale Selection
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
IEEE Transactions on Image Processing
A hybrid method for automatic anatomical variant detection and segmentation
FIMH'11 Proceedings of the 6th international conference on Functional imaging and modeling of the heart
Automatic multi-organ segmentation using learning-based segmentation and level set optimization
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
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Organ segmentation is a challenging problem on which recent progress has been made by incorporation of local image statistics that model the heterogeneity of structures outside of an organ of interest. However, most of these methods rely on landmark based segmentation, which has certain drawbacks. We propose to perform organ segmentation with a novel level set algorithm that incorporates local statistics via a highly efficient point tracking mechanism. Specifically, we compile statistics on these tracked points to allow for a local intensity profile outside of the contour and to allow for a local surface area penalty, which allows us to capture fine detail where it is expected. The local intensity and curvature models are learned through landmarks automatically embedded on the surface of the training shapes. We use Parzen windows to model the internal organ intensities as one distribution since this is sufficient for most organs. In addition, since the method is based on level sets, we are able to naturally take advantage of recent work on global shape regularization. We show state-of-the-art results on the challenging problems of liver and kidney segmentation.