This paper proposes a functional that assigns low `energy' to sets of subsets of the image domain consisting of a number of possibly overlapping near-circular regions of approximately a given radius: a `gas of circles'. The model can be used as a prior for object extraction whenever the objects conform to the `gas of circles' geometry, e.g. cells in biological images. Configurations are represented by a multi-layer phase field. Each layer has an associated function, regions being defined by thresholding. Intra-layer interactions assign low energy to configurations consisting of non-overlapping near-circular regions, while overlapping regions are represented in separate layers. Inter-layer interactions penalize overlaps. Here we present a theoretical and experimental analysis of the model.

%B International Conference on Pattern Recognition (ICPR) %I IEEE %C Tsukuba, Japan %P 1427 - 1430 %8 Nov 2012 %@ 978-1-4673-2216-4 %G eng %9 Conference paper %M 13324819 %0 Conference Paper %B International Conference on Pattern Recognition (ICPR) %D 2012 %T Simultaneous Affine Registration of Multiple Shapes %A Csaba Domokos %A Zoltan Kato %E Jan-Olof Eklundh %E Yuichi Ohta %E Steven Tanimoto %X

The problem of simultaneously estimating affine deformations between multiple objects occur in many applications. Herein, a direct method is proposed which provides the result as a solution of a linear system of equations without establishing correspondences between the objects. The key idea is to construct enough linearly independent equations using covariant functions, and then finding the solution simultaneously for all affine transformations. Quantitative evaluation confirms the performance of the method.

%B International Conference on Pattern Recognition (ICPR) %I IEEE %C Tsukuba, Japan %P 9 - 12 %8 Nov 2012 %@ 978-1-4673-2216-4 %G eng %9 Conference paper %M 13324478 %0 Conference Paper %B International Conference on Pattern Recognition (ICPR) %D 2012 %T Spectral clustering to model deformations for fast multimodal prostate registration %A Jhimli Mitra %A Zoltan Kato %A Soumya Ghose %A Desire Sidibe %A Robert Martí %A Xavier Lladó %A Oliver Arnau %A Joan C Vilanova %A Fabrice Meriaudeau %E Jan-Olof Eklundh %E Yuichi Ohta %E Steven Tanimoto %X

This paper proposes a method to learn deformation parameters off-line for fast multimodal registration of ultrasound and magnetic resonance prostate images during ultrasound guided needle biopsy. The registration method involves spectral clustering of the deformation parameters obtained from a spline-based nonlinear diffeomorphism between training magnetic resonance and ultrasound prostate images. The deformation models built from the principal eigen-modes of the clusters are then applied on a test magnetic resonance image to register with the test ultrasound prostate image. The deformation model with the least registration error is finally chosen as the optimal model for deformable registration. The rationale behind modeling deformations is to achieve fast multimodal registration of prostate images while maintaining registration accuracies which is otherwise computationally expensive. The method is validated for 25 patients each with a pair of corresponding magnetic resonance and ultrasound images in a leave-one-out validation framework. The average registration accuracies i.e. Dice similarity coefficient of 0.927 ± 0.025, 95% Hausdorff distance of 5.14 ± 3.67 mm and target registration error of 2.44 ± 1.17 mm are obtained by our method with a speed-up in computation time by 98% when compared to Mitra et al. [7].

%B International Conference on Pattern Recognition (ICPR) %I IEEE %C Tsukuba, Japan %P 2622 - 2625 %8 Nov 2012 %@ 978-1-4673-2216-4 %G eng %U http://hal.archives-ouvertes.fr/docs/00/71/09/43/PDF/ICPR_Jhimli.pdf %9 Conference paper %M 13325059