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A Mumford-Shah Functional based Variational Model with Contour, Shape, and Probability Prior information for Prostate Segmentation

Abstract: Inter patient shape, size and intensity variations of the prostate in transrectal ultrasound (TRUS) images challenge automatic segmentation of the prostate. In this paper we propose a variational model driven by Mumford-Shah (MS) functional for segmenting the prostate. Parametric representation of the implicit curve is derived from principal component analysis (PCA) of the signed distance representation of the labeled training data to impose shape prior. Posterior probability of the prostate region determined from random forest classification facilitates initialization and propagation of our model in a MS energy minimization framework. The proposed method achieves mean Dice similarity coefficient (DSC) value of 0.97±0.01, with a mean Hausdorff distance (HD) value of 1.73±0.24 mm when validated with 24 images from 6 datasets in a leave-one-patient-out validation framework. The model achieves statistically significant t-test p-value<;0.0001 in mean DSC and mean HD values compared to traditional statistical models of shape and appearance

Thanks to VALTEC 08-1-0039 of Generalitat de Catalunya, Spanish Science and Innovation grant nb. TIN2011-23704, Spain and Conseil R´egional de Bourgogne, France for funding the resea

© International Conference on Pattern Recognition, 2012, p. 121-124

IEEE Computer Society

Manager: Ministerio de Ciencia e Innovación (Espanya)
Author: Ghose, Soumya
Mitra, Jhimli
Oliver i Malagelada, Arnau
Martí Marly, Robert
Lladó Bardera, Xavier
Freixenet i Bosch, Jordi
Vilanova Busquets, Joan Carles
Comet i Batlle, Josep
Sidibé, Désiré
Meriaudeau, Fabrice
Date: 2012
Abstract: Abstract: Inter patient shape, size and intensity variations of the prostate in transrectal ultrasound (TRUS) images challenge automatic segmentation of the prostate. In this paper we propose a variational model driven by Mumford-Shah (MS) functional for segmenting the prostate. Parametric representation of the implicit curve is derived from principal component analysis (PCA) of the signed distance representation of the labeled training data to impose shape prior. Posterior probability of the prostate region determined from random forest classification facilitates initialization and propagation of our model in a MS energy minimization framework. The proposed method achieves mean Dice similarity coefficient (DSC) value of 0.97±0.01, with a mean Hausdorff distance (HD) value of 1.73±0.24 mm when validated with 24 images from 6 datasets in a leave-one-patient-out validation framework. The model achieves statistically significant t-test p-value<;0.0001 in mean DSC and mean HD values compared to traditional statistical models of shape and appearance
Thanks to VALTEC 08-1-0039 of Generalitat de Catalunya, Spanish Science and Innovation grant nb. TIN2011-23704, Spain and Conseil R´egional de Bourgogne, France for funding the resea
Format: application/pdf
ISSN: 1051-4651
Document access: http://hdl.handle.net/10256/13729
Language: eng
Publisher: IEEE Computer Society
Collection: MICINN/PN 2012-2012/TIN2011-23704
Reproducció digital del document publicat a: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6460087&tag=1
Articles publicats (D-ATC)
Is part of: © International Conference on Pattern Recognition, 2012, p. 121-124
Rights: Tots els drets reservats
Subject: Pròstata -- Càncer -- Imatges
Prostate -- Cancer -- Imaging
Imatgeria mèdica
Imaging systems in medicine
Title: A Mumford-Shah Functional based Variational Model with Contour, Shape, and Probability Prior information for Prostate Segmentation
Type: info:eu-repo/semantics/article
Repository: DUGiDocs

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