Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast level set method for propagating interfaces
Journal of Computational Physics
Valmet: A New Validation Tool for Assessing and Improving 3D Object Segmentation
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Level-Set Evolution with Region Competition: Automatic 3-D Segmentation of Brain Tumors
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Threshold-based 3D Tumor Segmentation using Level Set (TSL)
WACV '07 Proceedings of the Eighth IEEE Workshop on Applications of Computer Vision
Detection and segmentation of pathological structures by the extended graph-shifts algorithm
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Segmenting brain tumors with conditional random fields and support vector machines
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
3D brain tumor segmentation using fuzzy classification and deformable models
WILF'05 Proceedings of the 6th international conference on Fuzzy Logic and Applications
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Computers in Biology and Medicine
Iris plaque detection method based on level set
CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition
Segmentation of interest region in medical volume images using geometric deformable model
Computers in Biology and Medicine
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The level set approach can be used as a powerful tool for 3D segmentation of a tumor to achieve an accurate estimation of its volume. A major challenge of such algorithms is to set the equation parameters, especially the speed function. In this paper, we introduce a threshold-based scheme that uses level sets for 3D tumor segmentation (TLS). In this scheme, the level set speed function is designed using a global threshold. This threshold is defined based on the idea of confidence interval and is iteratively updated throughout the evolution process. We propose two threshold-updating schemes, search-based and adaptive, that require different degrees of user involvement. TLS does not require explicit knowledge about the tumor and non-tumor density functions and can be implemented in an automatic or semi-automatic form depending on the complexity of the tumor shape. The proposed algorithm has been tested on magnetic resonance images of the head for tumor segmentation and its performance evaluated visually and quantitatively. The experimental results confirm the effectiveness of TLS and its superior performance when compared with a region-competition based method.