P. Rodríguez, R. Dosil, X. M. Pardo, V. Leborán Grupo de Visión Artificial Departamento de Electrónica e Computación Universidade de Santiago de Compostela Automatic Generation of Initial Surfaces for Implicit Snakes

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

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

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

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

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

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

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

Global Shape Model  Superquadrics with global deformations  Few parameters bring structural information  Global Deformations: asymmetry  Implicit equation

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

Methodology I.Prior model construction from sample images  Optimization with Genetic Algorithms  Minimization of error function: whereand Average Surface Prior Model I. Modeling

Methodology II.Image feature extraction 1.Smoothing by anisotropic diffusion 2.Non gradient maxima suppression 3.Hysteresis thresholding Volume Data Surface Patches II. Preprocessing

Methodology III.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 Surface Patches III. Matching Initial Model Prior Model

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

Results

Results

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