<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Csaba Molnar</style></author><author><style face="normal" font="default" size="100%">Zoltan Kato</style></author><author><style face="normal" font="default" size="100%">Ian Jermyn</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Jan-Olof Eklundh</style></author><author><style face="normal" font="default" size="100%">Yuichi Ohta</style></author><author><style face="normal" font="default" size="100%">Steven Tanimoto</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A Multi-Layer Phase Field Model for Extracting Multiple Near-Circular Objects</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Pattern Recognition (ICPR)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Nov 2012</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Tsukuba, Japan</style></pub-location><pages><style face="normal" font="default" size="100%">1427 - 1430</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4673-2216-4 </style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;div class=&quot;article&quot;&gt;&lt;p&gt;This paper proposes a functional that assigns low `energy' to sets of subsets of the image domain consisting of a number of possibly overlapping &lt;span class=&quot;snippet&quot;&gt;near&lt;/span&gt;-&lt;span class=&quot;snippet&quot;&gt;circular&lt;/span&gt; regions of approximately a given radius: a `gas of circles'. The &lt;span class=&quot;snippet&quot;&gt;model&lt;/span&gt; can be used as a prior for &lt;span class=&quot;snippet&quot;&gt;object&lt;/span&gt; extraction whenever the &lt;span class=&quot;snippet&quot;&gt;objects&lt;/span&gt; conform to the `gas of circles' geometry, e.g. cells in biological images. Configurations are represented by a &lt;span class=&quot;snippet&quot;&gt;multi&lt;/span&gt;-&lt;span class=&quot;snippet&quot;&gt;layer&lt;/span&gt; &lt;span class=&quot;snippet&quot;&gt;phase&lt;/span&gt; &lt;span class=&quot;snippet&quot;&gt;field&lt;/span&gt;. Each &lt;span class=&quot;snippet&quot;&gt;layer&lt;/span&gt; has an associated function, regions being defined by thresholding. Intra-&lt;span class=&quot;snippet&quot;&gt;layer&lt;/span&gt; interactions assign low energy to configurations consisting of non-overlapping &lt;span class=&quot;snippet&quot;&gt;near&lt;/span&gt;-&lt;span class=&quot;snippet&quot;&gt;circular&lt;/span&gt; regions, while overlapping regions are represented in separate layers. Inter-&lt;span class=&quot;snippet&quot;&gt;layer&lt;/span&gt; interactions penalize overlaps. Here we present a theoretical and experimental analysis of the &lt;span class=&quot;snippet&quot;&gt;model&lt;/span&gt;.&lt;/p&gt;&lt;/div&gt;&lt;p&gt;&amp;nbsp;&lt;/p&gt;</style></abstract><work-type><style face="normal" font="default" size="100%">Conference paper</style></work-type><accession-num><style face="normal" font="default" size="100%">13324819</style></accession-num></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Csaba Domokos</style></author><author><style face="normal" font="default" size="100%">Zoltan Kato</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Jan-Olof Eklundh</style></author><author><style face="normal" font="default" size="100%">Yuichi Ohta</style></author><author><style face="normal" font="default" size="100%">Steven Tanimoto</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Simultaneous Affine Registration of Multiple Shapes</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Pattern Recognition (ICPR)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Nov 2012</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Tsukuba, Japan</style></pub-location><pages><style face="normal" font="default" size="100%">9 - 12</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4673-2216-4 </style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;div class=&quot;article&quot;&gt;&lt;p&gt;The problem of simultaneously estimating &lt;span class=&quot;snippet&quot;&gt;affine&lt;/span&gt; deformations between &lt;span class=&quot;snippet&quot;&gt;multiple&lt;/span&gt; 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 &lt;span class=&quot;snippet&quot;&gt;affine&lt;/span&gt; transformations. Quantitative evaluation confirms the performance of the method.&lt;/p&gt;&lt;/div&gt;&lt;p&gt;&amp;nbsp;&lt;/p&gt;</style></abstract><work-type><style face="normal" font="default" size="100%">Conference paper</style></work-type><accession-num><style face="normal" font="default" size="100%">13324478 </style></accession-num></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jhimli Mitra</style></author><author><style face="normal" font="default" size="100%">Zoltan Kato</style></author><author><style face="normal" font="default" size="100%">Soumya Ghose</style></author><author><style face="normal" font="default" size="100%">Desire Sidibe</style></author><author><style face="normal" font="default" size="100%">Robert Martí</style></author><author><style face="normal" font="default" size="100%">Xavier Lladó</style></author><author><style face="normal" font="default" size="100%">Oliver Arnau</style></author><author><style face="normal" font="default" size="100%">Joan C Vilanova</style></author><author><style face="normal" font="default" size="100%">Fabrice Meriaudeau</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Jan-Olof Eklundh</style></author><author><style face="normal" font="default" size="100%">Yuichi Ohta</style></author><author><style face="normal" font="default" size="100%">Steven Tanimoto</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Spectral clustering to model deformations for fast multimodal prostate registration</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Pattern Recognition (ICPR)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Nov 2012</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://hal.archives-ouvertes.fr/docs/00/71/09/43/PDF/ICPR_Jhimli.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Tsukuba, Japan</style></pub-location><pages><style face="normal" font="default" size="100%">2622 - 2625</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4673-2216-4 </style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;div class=&quot;article&quot;&gt;&lt;p&gt;This paper proposes a method &lt;span class=&quot;snippet&quot;&gt;to&lt;/span&gt; learn &lt;span class=&quot;snippet&quot;&gt;deformation&lt;/span&gt; parameters off-line for &lt;span class=&quot;snippet&quot;&gt;fast&lt;/span&gt; &lt;span class=&quot;snippet&quot;&gt;multimodal&lt;/span&gt; &lt;span class=&quot;snippet&quot;&gt;registration&lt;/span&gt; of ultrasound and magnetic resonance &lt;span class=&quot;snippet&quot;&gt;prostate&lt;/span&gt; images during ultrasound guided needle biopsy. The &lt;span class=&quot;snippet&quot;&gt;registration&lt;/span&gt; method involves &lt;span class=&quot;snippet&quot;&gt;spectral&lt;/span&gt; &lt;span class=&quot;snippet&quot;&gt;clustering&lt;/span&gt; of the &lt;span class=&quot;snippet&quot;&gt;deformation&lt;/span&gt; parameters obtained from a spline-based nonlinear diffeomorphism between training magnetic resonance and ultrasound &lt;span class=&quot;snippet&quot;&gt;prostate&lt;/span&gt; images. The &lt;span class=&quot;snippet&quot;&gt;deformation&lt;/span&gt; &lt;span class=&quot;snippet&quot;&gt;models&lt;/span&gt; built from the principal eigen-modes of the &lt;span class=&quot;snippet&quot;&gt;clusters&lt;/span&gt; are then applied on a test magnetic resonance image &lt;span class=&quot;snippet&quot;&gt;to&lt;/span&gt; register with the test ultrasound &lt;span class=&quot;snippet&quot;&gt;prostate&lt;/span&gt; image. The &lt;span class=&quot;snippet&quot;&gt;deformation&lt;/span&gt; &lt;span class=&quot;snippet&quot;&gt;model&lt;/span&gt; with the least &lt;span class=&quot;snippet&quot;&gt;registration&lt;/span&gt; error is finally chosen as the optimal &lt;span class=&quot;snippet&quot;&gt;model&lt;/span&gt; for deformable &lt;span class=&quot;snippet&quot;&gt;registration&lt;/span&gt;. The rationale behind &lt;span class=&quot;snippet&quot;&gt;modeling&lt;/span&gt; &lt;span class=&quot;snippet&quot;&gt;deformations&lt;/span&gt; is &lt;span class=&quot;snippet&quot;&gt;to&lt;/span&gt; achieve &lt;span class=&quot;snippet&quot;&gt;fast&lt;/span&gt; &lt;span class=&quot;snippet&quot;&gt;multimodal&lt;/span&gt; &lt;span class=&quot;snippet&quot;&gt;registration&lt;/span&gt; of &lt;span class=&quot;snippet&quot;&gt;prostate&lt;/span&gt; images while maintaining &lt;span class=&quot;snippet&quot;&gt;registration&lt;/span&gt; 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 &lt;span class=&quot;snippet&quot;&gt;registration&lt;/span&gt; accuracies i.e. Dice similarity coefficient of 0.927 ± 0.025, 95% Hausdorff distance of 5.14 ± 3.67 mm and target &lt;span class=&quot;snippet&quot;&gt;registration&lt;/span&gt; error of 2.44 ± 1.17 mm are obtained by our method with a speed-up in computation time by 98% when compared &lt;span class=&quot;snippet&quot;&gt;to&lt;/span&gt; Mitra et al. [7].&lt;/p&gt;&lt;/div&gt;&lt;p&gt;&amp;nbsp;&lt;/p&gt;</style></abstract><work-type><style face="normal" font="default" size="100%">Conference paper</style></work-type><accession-num><style face="normal" font="default" size="100%">13325059</style></accession-num></record></records></xml>