Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Algorithms for Low-Level Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Methods and Programs in Biomedicine
Multi-threshold level set model for image segmentation
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
A review of atlas-based segmentation for magnetic resonance brain images
Computer Methods and Programs in Biomedicine
Automated PET-guided liver segmentation from low-contrast CT volumes using probabilistic atlas
Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine
Segmentation of abdominal organs from CT using a multi-level, hierarchical neural network strategy
Computer Methods and Programs in Biomedicine
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The present study developed a hybrid semi-automatic method to extract the liver from abdominal computerized tomography (CT) images. The proposed hybrid method consists of a customized fast-marching level-set method for detection of an optimal initial liver region from multiple seed points selected by the user and a threshold-based level-set method for extraction of the actual liver region based on the initial liver region. The performance of the hybrid method was compared with those of the 2D region growing method implemented in OsiriX using abdominal CT datasets of 15 patients. The hybrid method showed a significantly higher accuracy in liver extraction (similarity index, SI=97.6+/-0.5%; false positive error, FPE=2.2+/-0.7%; false negative error, FNE=2.5+/-0.8%; average symmetric surface distance, ASD=1.4+/-0.5mm) than the 2D (SI=94.0+/-1.9%; FPE=5.3+/-1.1%; FNE=6.5+/-3.7%; ASD=6.7+/-3.8mm) region growing method. The total liver extraction time per CT dataset of the hybrid method (77+/-10s) is significantly less than the 2D region growing method (575+/-136s). The interaction time per CT dataset between the user and a computer of the hybrid method (28+/-4s) is significantly shorter than the 2D region growing method (484+/-126s). The proposed hybrid method was found preferred for liver segmentation in preoperative virtual liver surgery planning.