1. Introduction
2. Fundamental Issues of BUS Image Segmentation
2.1 Denoising and Preserving Edge
2.2 Human Intervention
2.3 Modeling Prior Knowledge
2.4 Validation: Ground Truth, Metrics, and BUS Image Datasets
3. Graph-Based Approaches
3.1 MRF-MAP Approaches
3.2 Graph Cuts
4. Deformable Models
4.1 Parametric Deformable Models
4.2 Geometric Deformable Models (GDMs)
4.3 Edge-Based vs. Region-Based DMs
4.4 Deformable Models for BUS Image Segmentation
5. Learning-Based Approaches
5.1 Unsupervised Learning Approaches
5.2 Supervised Learning Approaches
6. Classical Approaches: Thresholding, Region Growing, and Watershed
6.1 Thresholding
6.2 Region Growing
7. Other Approaches
8. Conclusions and Future Directions