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 Cavallaro School 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

Approach: rigid registration 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

Example

Our approach Rigid registration based on landmarks Landmark detection via Statistical Shape Analysis

BtG project: Achievement 1 Improved accuracy Red – before BtG Green – after BtG

Approach: overview Detection of Test Scan Landmark Points Coarse registration using Key landmarks Fine registration using the Semantic Regions Detection of Stable regions Distance estimation Detection of Landmark Points Reference scan

Approach: overview Detection of Test Scan Landmark Points Coarse registration using Key landmarks Fine registration using the Semantic Regions Detection of Stable regions Distance estimation Detection of Landmark Points Reference scan Test scan Reference scan

Approach: overview Detection of Test Scan Landmark Points Coarse registration using Key landmarks Fine registration using the Semantic Regions Detection of Stable regions Distance estimation Detection of Landmark Points Reference scan Test scan Reference scan

Approach: overview Detection of Test Scan Landmark Points Coarse registration using Key landmarks Fine registration using the Semantic Regions Detection of Stable regions Distance estimation Detection of Landmark Points Reference scan Coarse registration Test scan Reference scan Key Landmarks

Approach: overview Detection of Test Scan Landmark Points Coarse registration using Key landmarks Fine registration using the Semantic Regions Detection of Stable regions Distance estimation Detection of Landmark Points Reference scan Test scan Reference scan

Approach: overview Detection of Test Scan Landmark Points Coarse registration using Key landmarks Fine registration using the Semantic Regions Detection of Stable regions Distance estimation Detection of Landmark Points Reference scan Fine registration Test scan Reference scan

Approach: overview Detection of Test Scan Landmark Points Coarse registration using Key landmarks Fine registration using the Semantic Regions Detection of Stable regions Distance estimation Detection of Landmark Points Reference scan

Example ICP Proposed approach

BtG project: Achievement 2 User friendly GUI To ease burden on clinicians User-feedback mechanisms

Conclusions Achievements Current work 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: prathap.nair@elec.qmul.ac.uk andrea.cavallaro@elec.qmul.ac.uk