Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
An improved level set for liver segmentation and perfusion analysis in MRIs
IEEE Transactions on Information Technology in Biomedicine
Diagnosis of liver disease by using CMAC neural network approach
Expert Systems with Applications: An International Journal
Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation
Computers in Biology and Medicine
Robust kernel FCM in segmentation of breast medical images
Expert Systems with Applications: An International Journal
Level set diffusion for MRE image enhancement
MIAR'10 Proceedings of the 5th international conference on Medical imaging and augmented reality
A novel case based reasoning approach to radiotherapy planning
Expert Systems with Applications: An International Journal
Shape recovery algorithms using level sets in 2-D/3-D medical imagery: a state-of-the-art review
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Image Processing
Level Set Segmentation With Multiple Regions
IEEE Transactions on Image Processing
MICCAI'12 Proceedings of the 4th international conference on Abdominal Imaging: computational and clinical applications
Computers in Biology and Medicine
Hi-index | 12.05 |
Objective: Computerized liver tumor segmentation on computed tomography (CT) images is a challenging problem. Level set methods have been proposed for CT liver and tumor segmentation. However, the common models using image gradient or region competition have inherent drawbacks, and are not very robust for liver tumor segmentation. Methods: We propose a new unified level set model to integrate image gradient, region competition and prior information for CT liver tumor segmentation. The probabilistic distribution of liver tumors is estimated by unsupervised fuzzy clustering, and is utilized to enhance the object indication function, define the directional balloon force and regulate region competition. This unified model has been evaluated on 25 two-dimensional (2D) CT scans and 4 three-dimensional (3D) CT scans with 10 tumors. Results: For the 2D dataset, the area overlapping error (AOE) is 12.75+/-5.76%, the relative area difference (RAD) is -4.28+/-9.58%, the average contour distance (ACD) is 1.66+/-1.09mm, and the maximum contour distance (MCD) is 4.29+/-2.75mm. For the 3D dataset, the volume overlapping error (VOE) is 26.31+/-5.79%, the relative volume difference (RVD) is -10.64+/-7.55%, the average surface distance (ASD) is 1.06+/-0.38mm, and the maximum surface distance (MSD) is 8.66+/-3.17mm. All results are competitive with that of the state-of-the-art methods. Conclusion: The new unified level set model is an effective solution for liver tumor segmentation on contrast-enhanced CT images.