Automatic Generation of Initial Surfaces for Implicit Snakes

1. Automatic Generation ofInitial Surfaces for Implicit Snakes P. Rodríguez, R. Dosil, X. M. Pardo, V. Leborán Grupo de Visión ArtificialDepartamento de Electrónica e ComputaciónUniversidade de Santiago de Compostela

2. Introduction Global Shape Model CSG Model Superquadric primitives Methodology Prior Model Construction Image Feature Extraction Matching Results and Conclusions Outline

3. Introduction • 3D surface reconstruction: Segmentation with deformable models • Good local approximation • Need of good initial estimation

4. Introduction • Previous Solutions • Manual initialization: • is not practical in 3D • Landmark registration: • landmarks are not always identifiable • Part decomposition techniques • need of joint detection or part recovery • lack of robustness when data is incomplete or noisy

5. Introduction • Objectives • Automatic initialization of 3D medical images(CT, MRI, …) • No use of landmarks • Application to multi-part objects • Robustness to noise and presence of other objects

6. Introduction • Proposal:matching with multi-part prior models • Initialization by matching with prior models • Robustness • No need of part or joint detection • Use of composite global shape models • Multi-part models: CSG • Primitives: Superquadrics • Image features are image surface points • No use of landmarks

7. Matching between surface model and object surface points • Prior model construction from sample images • Object surface points extraction Volume Data Average Surface II. Preprocessing I. Modeling Surface Patches Prior Model III. Matching Initial Model Introduction

8. Global Shape Model • Constructive Solid Geometry (CSG) • Binary tree • Leaf nodes: solid primitives • Internal nodes: Boolean operations • Arcs: rigid transformations • Primitives: Superquadrics with global deformations

9. Global Shape Model • Superquadrics with global deformations • Few parameters bring structural information • Global Deformations: asymmetry • Implicit equation

10. Average Surface I. Modeling Prior Model Methodology • Prior model construction from sample images • Manual part decomposition • Individual modeling of object parts • Shape parameters • Relative spatial distribution parameters

11. Average Surface I. Modeling Prior Model Methodology • Prior model construction from sample images • Optimization with Genetic Algorithms • Minimization of error function: where and

12. Volume Data II. Preprocessing Surface Patches Methodology • Image feature extraction • Smoothing by anisotropic diffusion • Non gradient maxima suppression • Hysteresis thresholding

13. Prior Model Surface Patches III. Matching Initial Model Methodology • Matching between model and object features • Find global rigid transformation T such that the transformed model fits the object surface • GA to minimize error function

14. Methodology • Matching between model and object features • Radial distance to a deformed implicit surface is difficult to calculate • The following approximation is used

15. Results

16. Results

17. Conclusions • Contributions • Automatization of initialization • Easy handling of multipart shapes using a compound model • No part or joint detection • Easy optimization of the model • Future work • Introduction of fine tuning of individual part parameters • Incorporation of other Boolean operations to the CSG model to handle concavities