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Special Semester on Quantitative Biology analyzed by Mathematical Methods
Linz, October 1, 2007 - January 27, 2008
Metric-Free Dense Image Registration Using Primal/Dual Principles and Efficient Linear Programming

Workshop on Bioimaging II / PDEs, Wed, 21 Nov, 2007

Speaker: Nikos Paragios

Abstract

In this talk, we present a novel dynamic discrete framework to address dense image morphing with applications to multi-modal image registration and surface alignment. We reformulate the problem using a number of discrete deformations (displacements), and therefore the estimation of the morphing parameters becomes a tractable similarity metric-independent combinatorial problem which is solved through the FastPD algorithm. In order to overcome the main limitation of discrete approaches (low dimensionality of the label space is unable to capture the continuous nature of the expected solution), we introduce a dynamic behavior in the model where the plausible discrete deformations (displacements) are varying in space (across the domain) and time (different states of the process - successive morphing states) according to the local uncertainty of the obtained solution. Therefore, the method is gradient free, can encode various similarity metrics (simple changes on the graph construction), can guarantee a globally sub-optimal solution and is computational tractable. Experimental validation using simulated data with known deformation, as well as manually segmented data demonstrate the extreme potentials of our approach.

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