Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
International Journal of Computer Vision
User-steered image segmentation paradigms: live wire and live lane
Graphical Models and Image Processing
Interactive segmentation with Intelligent Scissors
Graphical Models and Image Processing
Gradient flows and geometric active contour models
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
IEEE Transactions on Image Processing
Interactive Image Segmentation via Adaptive Weighted Distances
IEEE Transactions on Image Processing
Exploiting user labels with generalized distance transforms random field level sets
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Interactive segmentation of 3D images using a region adjacency graph representation
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
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Image-guided therapy procedures require the patient to remain still throughout the image acquisition, data analysis and therapy. This imposes a tight time constraint on the overall process. Automatic extraction of the pathological regions prior to the therapy can be faster than the customary manual segmentation performed by the physician. However, the image data alone is usually not sufficient for reliable and unambiguous computerized segmentation. Thus, the oversight of an experienced physician remains mandatory. We present a novel segmentation framework, that allows user feedback. A few mouse-clicks of the user, discrete in nature, are represented as a continuous energy term that is incorporated into a level-set functional. We demonstrate the proposed method on MR scans of uterine fibroids acquired prior to focused ultrasound ablation treatment. The experiments show that with a minimal user input, automatic segmentation results become practically identical to manual expert segmentation.