International Journal of Computer Vision
Model Library for Deformable Model-Based Segmentation of 3-D Brain MR-Images
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
Computing Geodesics and Minimal Surfaces via Graph Cuts
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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
IEEE Transactions on Image Processing
Optimal Weights for Convex Functionals in Medical Image Segmentation
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Adaptive Contextual Energy Parameterization for Automated Image Segmentation
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Adaptive regularization for image segmentation using local image curvature cues
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
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Energy functional minimization is an increasingly popular technique for image segmentation. However, it is far too commonly applied with hand-tuned parameters and initializations that have only been validated for a few images. Fixing these parameters over a set of images assumes the same parameters are ideal for each image. We highlight the effects of varying the parameters and initialization on segmentation accuracy and propose a framework for attaining improved results using image adaptive parameters and initializations. We provide an analytical definition of optimal weights for functional terms through an examination of segmentation in the context of image manifolds, where nearby images on the manifold require similar parameters and similar initializations. Our results validate that fixed parameters are insufficient in addressing the variability in real clinical data, that similar images require similar parameters, and demonstrate how these parameters correlate with the image manifold. We present significantly improved segmentations for synthetic images and a set of 470 clinical examples.