Shape Priors for Level Set Representations
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Segmentation of Dynamic N-D Data Sets via Graph Cuts Using Markov Models
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
On the Incorporation of shape priors into geometric active contours
VLSM '01 Proceedings of the IEEE Workshop on Variational and Level Set Methods (VLSM'01)
Alignment by maximization of mutual information
Alignment by maximization of mutual information
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Precise segmentation of multimodal images
IEEE Transactions on Image Processing
Analysis of MR Images of Mice in Preclinical Treatment Monitoring of Polycystic Kidney Disease
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Non-parametric iterative model constraint graph min-cut for automatic kidney segmentation
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
A fully automated approach to segmentation of irregularly shaped cellular structures in EM images
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Automated segmentation of 3D CT images based on statistical atlas and graph cuts
MCV'10 Proceedings of the 2010 international MICCAI conference on Medical computer vision: recognition techniques and applications in medical imaging
Orientation histograms as shape priors for left ventricle segmentation using graph cuts
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
A fully automated framework for renal cortex segmentation
MICCAI'12 Proceedings of the 4th international conference on Abdominal Imaging: computational and clinical applications
MCV'12 Proceedings of the Second international conference on Medical Computer Vision: recognition techniques and applications in medical imaging
Kidney segmentation using graph cuts and pixel connectivity
Pattern Recognition Letters
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We propose a novel kidney segmentation approach based on the graph cuts technique. The proposed approach depends on both image appearance and shape information. Shape information is gathered from a set of training shapes. Then we estimate the shape variations using a new distance probabilistic model which approximates the marginal densities of the kidney and its background in the variability region using a Poisson distribution refined by positive and negative Gaussian components. To segment a kidney slice, we align it with the training slices so we can use the distance probabilistic model. Then its gray level is approximated with a LCG with sign-alternate components. The spatial interaction between the neighboring pixels is identified using a new analytical approach. Finally, we formulate a new energy function using both image appearance models and shape constraints. This function is globally minimized using s/t graph cuts to get the optimal segmentation. Experimental results show that the proposed technique gives promising results compared to others without shape constraints.