landmark localization and registration of 3d facial scans
TRANSCRIPT
Landmark localization and registration of 3D facial scans
for the evaluation of orthodontic treatments in maxillofacial and oral surgery
School of EECS: Prathap Nair, Dr Andrea CavallaroSchool of Medicine and Dentistry: Dr Lifong Zou
Mid-project update
What is the problem?
• To quantify 3D facial asymmetry
• Clinical diagnosis• Treatment planning• Post-treatment monitoring• Statistical studies on a large population
• What is rigid registration?• Alignment of 2 or more faces
• Classical approach: Iterative Closest Point (ICP) algorithm• Advantage
• no prior info needed• Disadvantage
• random points used for matching can lead to erroneous results
Approach: rigid registration
Example
Our approach
• Rigid registration based on landmarks • Landmark detection via Statistical Shape Analysis
BtG project: Achievement 1
• Improved accuracy
Red – before BtGGreen – after BtG
Approach: overview
Test Scan
Reference scan
Detection of
Landmark Points
Detection of
Landmark Points
Coarse registratio
n using Key
landmarks
Detection of
Stable regions
Fine registration using
the Semantic Regions
Distanceestimatio
n
Test scan Reference scan
Approach: overview
Test Scan
Reference scan
Detection of
Landmark Points
Detection of
Landmark Points
Coarse registratio
n using Key
landmarks
Detection of
Stable regions
Fine registration using
the Semantic Regions
Distanceestimatio
n
Test scan Reference scan
Approach: overview
Test Scan
Reference scan
Detection of
Landmark Points
Detection of
Landmark Points
Coarse registratio
n using Key
landmarks
Detection of
Stable regions
Fine registration using
the Semantic Regions
Distanceestimatio
n
Test scan Reference scan
Approach: overview
Test Scan
Reference scan
Detection of
Landmark Points
Detection of
Landmark Points
Coarse registratio
n using Key
landmarks
Detection of
Stable regions
Fine registration using
the Semantic Regions
Distanceestimatio
n
Key Landmar
ks
Coarse registration
Test scan Reference scan
Approach: overview
Test Scan
Reference scan
Detection of
Landmark Points
Detection of
Landmark Points
Coarse registratio
n using Key
landmarks
Detection of
Stable regions
Fine registration using
the Semantic Regions
Distanceestimatio
n
Test scan Reference scan
Approach: overview
Test Scan
Reference scan
Detection of
Landmark Points
Detection of
Landmark Points
Coarse registratio
n using Key
landmarks
Detection of
Stable regions
Fine registration using
the Semantic Regions
Distanceestimatio
n
Fine registration
Approach: overview
Test Scan
Reference scan
Detection of
Landmark Points
Detection of
Landmark Points
Coarse registratio
n using Key
landmarks
Detection of
Stable regions
Fine registration using
the Semantic Regions
Distanceestimatio
n
ICP Proposed approach
Example
BtG project: Achievement 2
• User friendly GUI• To ease burden on clinicians• User-feedback mechanisms
Conclusions
• Achievements• Improved landmark localisation accuracy • More user-friendly GUI with the user feedback
• Current work• Clinical evaluation of the landmark detection accuracy • Validation of 3D facial scan registration accuracy• Further improving the GUI based on clinician feedback
Contact: