# Algorithm Comparison

This table compares all available algorithms to help you choose the right one for your use case.

| Algorithm | Speed | Precision | Best For |
|-----------|-------|-----------|----------|
| [Geodesic Distance](geodesic.md) | Fast | High | General use, most tissue types |
| [Watershed](watershed.md) | Medium | High | Marker-based segmentation, clear boundaries |
| [Random Walker](random_walker.md) | Medium | High | Noisy images, probabilistic segmentation |
| [Level Set](level_set.md) | Slow | Very High | Irregular boundaries, high precision needed |
| [Connected Threshold](connected_threshold.md) | Very Fast | Low | Quick rough segmentation, uniform regions |
| [Region Growing](region_growing.md) | Fast | Medium | Homogeneous regions with clear boundaries |
| [Threshold Brush](threshold_brush.md) | Very Fast | Variable | Simple threshold painting, when boundaries are clear |

## When to Use Each Algorithm

### For Speed
If you need fast results, use **Threshold Brush** or **Connected Threshold**.

### For Precision
If you need precise boundaries, use **Level Set** or **Geodesic Distance**.

### For Noisy Images
If your image has noise, use **Random Walker** which is robust to noise.

### For General Use
**Geodesic Distance** is the recommended default for most use cases.