01975nas a2200193 4500008004100000020002300041245008200064210006900146260003500215300001600250520128600266100001801552700001701570700001601587700002201603700001701625700002101642856011801663 2012 eng d a978-1-4673-2216-4 00aA Multi-Layer Phase Field Model for Extracting Multiple Near-Circular Objects0 aMultiLayer Phase Field Model for Extracting Multiple NearCircula aTsukuba, JapanbIEEEcNov 2012 a1427 - 14303 a

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.

 

1 aMolnar, Csaba1 aKato, Zoltan1 aJermyn, Ian1 aEklundh, Jan-Olof1 aOhta, Yuichi1 aTanimoto, Steven uhttps://inf.u-szeged.hu/publication/a-multi-layer-phase-field-model-for-extracting-multiple-near-circular-objects01245nas a2200181 4500008004100000020002300041245005600064210005600120260003500176300001100211520065300222100001900875700001700894700002200911700001700933700002100950856009200971 2012 eng d a978-1-4673-2216-4 00aSimultaneous Affine Registration of Multiple Shapes0 aSimultaneous Affine Registration of Multiple Shapes aTsukuba, JapanbIEEEcNov 2012 a9 - 123 a

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.

 

1 aDomokos, Csaba1 aKato, Zoltan1 aEklundh, Jan-Olof1 aOhta, Yuichi1 aTanimoto, Steven uhttps://inf.u-szeged.hu/publication/simultaneous-affine-registration-of-multiple-shapes03162nas a2200265 4500008004100000020002300041245008800064210006900152260003500221300001600256520231800272100001802590700001702608700001802625700001902643700001902662700001902681700001802700700002102718700002402739700002202763700001702785700002102802856007302823 2012 eng d a978-1-4673-2216-4 00aSpectral clustering to model deformations for fast multimodal prostate registration0 aSpectral clustering to model deformations for fast multimodal pr aTsukuba, JapanbIEEEcNov 2012 a2622 - 26253 a

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].

 

1 aMitra, Jhimli1 aKato, Zoltan1 aGhose, Soumya1 aSidibe, Desire1 aMartí, Robert1 aLladó, Xavier1 aArnau, Oliver1 aVilanova, Joan C1 aMeriaudeau, Fabrice1 aEklundh, Jan-Olof1 aOhta, Yuichi1 aTanimoto, Steven uhttp://hal.archives-ouvertes.fr/docs/00/71/09/43/PDF/ICPR_Jhimli.pdf