GVF Snake and Active Shape Model

1. GVF Snake and Active Shape Model Chunlei Han Turku PET Centre Sep 14, 2005, Turku, Finland

2. Content • Summary of GVF snake • Feature of medical image • Active shape model (ASM) • PET heart search algorithm

3. Summary of GVF snake 1 Tranditional force field GVF force field

4. Summary of GVF snake 2 Problems for GVF snake in applications, it • ideally works on binary image. • it still strongly depends on the initial position. • it does not use any prior knowledge to guide searching result.

5. Characteristics of Medical Image • ”multi-edge” image. • noisy. regularization constraints and prior knowledge are usually of goo use in applications

6. Active Shape Model (ASM) • Is a statistical shape model. • use the prior knowledge to guide/constrain result shape during searching process. • avoid unwanted result. • strongly depends on the prior model.

7. Basic idea of ASM • ASM is a statistical shape model. • Main idea of ASM are • Set up a statistical shape model using prior knowledge. • Using the prior model to guide and constrain the searching shape on real images.

8. Step 1. Training data set • Take a set of example shapes (Fig. 1). • Select a few landmark points (Fig. 2). • Do linear transformation (rotate,scale,align), then the landmark points should be places as in the same order and as ”in about the same place”. • Do statistical analysis for training data set (Fig. 3) Fig. 2, Landmark points Fig. 3, Point distribution around landmark points Fig. 1, Training data set

9. Step 2, Set up a model from training data set Step 3, Shape evolutes on image Based on image features, such as gray level, gradient and so on Step 4, Update model parameter and constraint Mapping evoluted shape back to model domain, update model parameter, b and constrain it.

10. Application examples of active shape model

11. PET Heart Search Algorithm • Set up a heart prior model, 2-D and 3-D • Using GVF snake to find local feature • Using ASM to update and constrain GVF snake during shape evolution