Computational and Statistical Joint Image Analysis

Image courtesy Graham Treece

Image analysis consists of many steps which start at the acquisition of the raw data and end with a fully reconstructed, motion corrected, registered and segmented image. Each of these steps consists of many possible algorithms but in almost all cases, the analysis proceeds linearly from one step to another, without taking into account the fact that errors are propagated from step to step.

This project will look at joint modelling and analysis of such issues from both a computational analysis and statistical point of view. Variational techniques and metric based procedures can be used to align and analyse the data jointly [1,2], while incorporating such metrics into the subsequent statistical modelling is also possible [3]. A dominant difficulty in joint models is the complexity of the latter, often resulting into nonlinear and non-convex variational problems whose analysis and robust computation are challenging.  Also the well-posedness and the statistical validity of a joint analysis is non-trivial, requiring sophisticated techniques from applied analysis and statistics. The project could take on various connotations, from very theoretical considerations to computational algorithm development and investigation of different applications in image analysis.

[1] S. Ozeré, C. Le Guyader, C. Gout, Joint segmentation/registration model by shape alignment via weighted total variation minimization and nonlinear elasticity, SIAM J. on Imaging Sciences 8(3), 1981–2020, 2015.
[2] H. Dirks, Variational Methods for Joint Motion Estimation and Image Reconstruction, PhD thesis, Institute for Computational and Applied Mathematics, University of Muenster,
[3] S Kurtek, H Drira. A comprehensive statistical framework for elastic shape analysis of 3D faces Computers & Graphics 51, 52–59, 2015

Who's involved

Papers, Publications & Software

MATLAB code for joint reconstruction of PET-MRI associated to the paper, Joint reconstruction of PET-MRI by exploiting structural similarity, Inverse Problems, Volume 31, Issue Number 1, 2014.

Matthias J. Ehrhardt

Graph clustering, variational image segmentation methods and Hough transform scale detection for object measurement in images

Luca Calatroni Yves van Gennip Carola-Bibiane Schönlieb Hannah Rowland Arjuna Flenner
Published 27/02/2016

Inference on covariance operators via concentration inequalities: k-sample tests, classification, and clustering via Rademacher complexities

Adam B Kashlak John AD Aston Richard Nickl
Published 21/04/2016
Variational methods & effective algorithms for imaging & vision programme started this week! @NewtonInstitute View on Twitter