6533b822fe1ef96bd127d82c
RESEARCH PRODUCT
A spline-based non-linear diffeomorphism for multimodal prostate registration.
Désiré SidibéJosep CometSoumya GhoseXavier LladóRobert MartíJoan C. VilanovaArnau OliverJhimli MitraFabrice MeriaudeauZoltan Katosubject
MaleProstate biopsyProstate -- Cancer -- DiagnosisPhysics::Medical Physics[INFO.INFO-IM] Computer Science [cs]/Medical ImagingHealth InformaticsSystem of linear equationsSensitivity and Specificity030218 nuclear medicine & medical imagingPattern Recognition AutomatedPròstata -- Càncer -- Diagnòstic03 medical and health sciences0302 clinical medicineArtificial IntelligenceImage Interpretation Computer-Assistedmedicine[INFO.INFO-IM]Computer Science [cs]/Medical ImagingBhattacharyya distanceHumansRadiology Nuclear Medicine and imagingComputer visionThin plate splineMathematicsUltrasonographyRadiological and Ultrasound Technologymedicine.diagnostic_test[ INFO.INFO-IM ] Computer Science [cs]/Medical Imagingbusiness.industryProstatic NeoplasmsReproducibility of ResultsProstate -- BiopsyImage EnhancementComputer Graphics and Computer-Aided DesignMagnetic Resonance ImagingPròstata -- BiòpsiaSpline (mathematics)Nonlinear systemHausdorff distanceNonlinear DynamicsComputer Science::Computer Vision and Pattern RecognitionSubtraction TechniqueImatgeria mèdicaComputer Vision and Pattern RecognitionDiffeomorphismArtificial intelligencebusiness030217 neurology & neurosurgeryAlgorithmsImaging systems in medicinedescription
This paper presents a novel method for non-rigid registration of transrectal ultrasound and magnetic resonance prostate images based on a non-linear regularized framework of point correspondences obtained from a statistical measure of shape-contexts. The segmented prostate shapes are represented by shape-contexts and the Bhattacharyya distance between the shape representations is used to find the point correspondences between the 2D fixed and moving images. The registration method involves parametric estimation of the non-linear diffeomorphism between the multimodal images and has its basis in solving a set of non-linear equations of thin-plate splines. The solution is obtained as the least-squares solution of an over-determined system of non-linear equations constructed by integrating a set of non-linear functions over the fixed and moving images. However, this may not result in clinically acceptable transformations of the anatomical targets. Therefore, the regularized bending energy of the thin-plate splines along with the localization error of established correspondences should be included in the system of equations. The registration accuracies of the proposed method are evaluated in 20 pairs of prostate mid-gland ultrasound and magnetic resonance images. The results obtained in terms of Dice similarity coefficient show an average of 0.980 ± 0.004, average 95% Hausdorff distance of 1.63 ± 0.48. mm and mean target registration and target localization errors of 1.60 ± 1.17. mm and 0.15 ± 0.12. mm respectively This work is a part of the PROSCAN Project of the VICOROB laboratory of University of Girona, Catalunya, Spain. The authors thank VALTEC 08-1-0039 of Generalitat de Catalunya, Spanish Science and Innovation grant nb. TIN2011-23704, Spain and Conseil Regional de Bourgogne, France for funding this research. The research is also partially supported by the Grant CNK80370 of the National Innovation Office (NIH) and Hungarian Scientific Research Fund (OTKA); the European Union and co-financed by the European Regional Development Fund within the Project TAMOP-4.2.1/B-09/1/KONV-2010-0005
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2012-09-01 | Medical image analysis |