Articles liés à Prostate Segmentation in Ultrasound and Magnetic Resonance...

Prostate Segmentation in Ultrasound and Magnetic Resonance Images - Couverture souple

Ghose, Soumya; Olivier, Arnau; Meriaudeau, Fabrice

 
9783659838453: Prostate Segmentation in Ultrasound and Magnetic Resonance Images
Couverture souple
ISBN 10 : 3659838454 ISBN 13 : 9783659838453
Editeur : Scholars' Press, 2016

Synopsis

Prostate segmentation in trans rectal ultrasound (TRUS) and magnetic resonance images (MRI) facilitates volume estimation, multi-modal image registration, surgical planing and image guided prostate biopsies. The objective of this work was to develop shape and region prior deformable models for accurate, robust and computationally efficient prostate segmentation in TRUS and MRI images. Primary contribution of this work was in adopting a probabilistic learning approach to achieve soft classification of the prostate for automatic initialization and evolution of a shape and region prior deformable models for prostate segmentation in TRUS images. Region based energy was determined from region based statistics of the posterior probabilities. Graph cut energy minimization framework was adopted for prostate segmentation in MRI. Posterior probabilities obtained in a supervised learning schema and from a probabilistic segmentation of the prostate using an atlas are fused in logarithmic domain to reduce segmentation error. Finally a graph cut energy minimization in the stochastic framework achieves prostate segmentation in MRI.

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Présentation de l'éditeur

Prostate segmentation in trans rectal ultrasound (TRUS) and magnetic resonance images (MRI) facilitates volume estimation, multi-modal image registration, surgical planing and image guided prostate biopsies. The objective of this work was to develop shape and region prior deformable models for accurate, robust and computationally efficient prostate segmentation in TRUS and MRI images. Primary contribution of this work was in adopting a probabilistic learning approach to achieve soft classification of the prostate for automatic initialization and evolution of a shape and region prior deformable models for prostate segmentation in TRUS images. Region based energy was determined from region based statistics of the posterior probabilities. Graph cut energy minimization framework was adopted for prostate segmentation in MRI. Posterior probabilities obtained in a supervised learning schema and from a probabilistic segmentation of the prostate using an atlas are fused in logarithmic domain to reduce segmentation error. Finally a graph cut energy minimization in the stochastic framework achieves prostate segmentation in MRI.

Biographie de l'auteur

Dr. Soumya Ghose is senior research associate in the Department of Biomedical Engineering at Case Western Reserve University. His research interest includes machine learning, medical image segmentation and registration. Prof. Meriaudeau and Dr. Olivier have extensive research experience in the domain of medical image processing and machine learning

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
Scholars' Press
Date d'édition
2016
Langue
anglais
ISBN 10 
3659838454
ISBN 13 
9783659838453
Reliure
Broché
Nombre de pages
152
Coordonnées du fabricant
non disponible
Personne responsable
non disponible