A new 3D automatic segmentation framework for accurate segmentation of prostate from DCE-MRI

A. Firjani; A. Elnakib; F. Khalifa; G. Gimel'Farb; M. Abo El-Ghar; J. Suri; A. Elmaghraby; A. El-Baz

doi:10.1109/ISBI.2011.5872679

A new 3D automatic segmentation framework for accurate segmentation of prostate from DCE-MRI

A. Firjani
, A. Elnakib
, F. Khalifa
, G. Gimel'Farb
, M. Abo El-Ghar
, J. Suri
, A. Elmaghraby
, A. El-Baz

School of Engineering (Behrend)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

14 Scopus citations

Abstract

Prostate segmentation is an essential step in developing any noninvasive Computer-Assisted Diagnostic (CAD) system for the early diagnosis of prostate cancer using Magnetic Resonance Images (MRI). In this paper, a novel framework for 3D segmentation of the prostate region from Dynamic Contrast Enhancement MRI (DCE-MRI) is proposed. The framework is based on Maximum A Posteriori (MAP) estimate of a new log-likelihood function that consists of : (i) 1 ^st-order visual appearance descriptors of the DCE-MRI, (ii) a 3D spatially rotation-variant 2^nd-order homogeneity descriptor, and (iii) a 3D prostate shape descriptor. The shape prior is learned from the co-aligned 3D segmented prostate data. The visual appearances of the object and its background are described with marginal gray-level distributions obtained by separating their mixture over prostate data. The spatial interactions between the prostate voxels are modeled by a 3D 2^nd-order rotation-variant Markov-Gibbs Random Field (MGRF) of object/background labels with analytically estimated potentials. Experiments with in vivo prostate DCE-MRI confirm the robustness and accuracy of the proposed approach.

Original language	English (US)
Title of host publication	2011 8th IEEE International Symposium on Biomedical Imaging
Subtitle of host publication	From Nano to Macro, ISBI'11
Pages	1476-1479
Number of pages	4
DOIs	https://doi.org/10.1109/ISBI.2011.5872679
State	Published - 2011
Event	2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11 - Chicago, IL, United States Duration: Mar 30 2011 → Apr 2 2011

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Other

Other	2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11
Country/Territory	United States
City	Chicago, IL
Period	3/30/11 → 4/2/11

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Biomedical Engineering
Radiology Nuclear Medicine and imaging

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10.1109/ISBI.2011.5872679

Cite this

Firjani, A., Elnakib, A., Khalifa, F., Gimel'Farb, G., Abo El-Ghar, M., Suri, J., Elmaghraby, A., & El-Baz, A. (2011). A new 3D automatic segmentation framework for accurate segmentation of prostate from DCE-MRI. In 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11 (pp. 1476-1479). Article 5872679 (Proceedings - International Symposium on Biomedical Imaging). https://doi.org/10.1109/ISBI.2011.5872679

@inproceedings{b9eb0f26fea0446da145e7b77de33d44,

title = "A new 3D automatic segmentation framework for accurate segmentation of prostate from DCE-MRI",

abstract = "Prostate segmentation is an essential step in developing any noninvasive Computer-Assisted Diagnostic (CAD) system for the early diagnosis of prostate cancer using Magnetic Resonance Images (MRI). In this paper, a novel framework for 3D segmentation of the prostate region from Dynamic Contrast Enhancement MRI (DCE-MRI) is proposed. The framework is based on Maximum A Posteriori (MAP) estimate of a new log-likelihood function that consists of : (i) 1 st-order visual appearance descriptors of the DCE-MRI, (ii) a 3D spatially rotation-variant 2nd-order homogeneity descriptor, and (iii) a 3D prostate shape descriptor. The shape prior is learned from the co-aligned 3D segmented prostate data. The visual appearances of the object and its background are described with marginal gray-level distributions obtained by separating their mixture over prostate data. The spatial interactions between the prostate voxels are modeled by a 3D 2nd-order rotation-variant Markov-Gibbs Random Field (MGRF) of object/background labels with analytically estimated potentials. Experiments with in vivo prostate DCE-MRI confirm the robustness and accuracy of the proposed approach.",

author = "A. Firjani and A. Elnakib and F. Khalifa and G. Gimel'Farb and \{Abo El-Ghar\}, M. and J. Suri and A. Elmaghraby and A. El-Baz",

year = "2011",

doi = "10.1109/ISBI.2011.5872679",

language = "English (US)",

isbn = "9781424441280",

series = "Proceedings - International Symposium on Biomedical Imaging",

pages = "1476--1479",

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Firjani, A, Elnakib, A, Khalifa, F, Gimel'Farb, G, Abo El-Ghar, M, Suri, J, Elmaghraby, A & El-Baz, A 2011, A new 3D automatic segmentation framework for accurate segmentation of prostate from DCE-MRI. in 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11., 5872679, Proceedings - International Symposium on Biomedical Imaging, pp. 1476-1479, 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11, Chicago, IL, United States, 3/30/11. https://doi.org/10.1109/ISBI.2011.5872679

A new 3D automatic segmentation framework for accurate segmentation of prostate from DCE-MRI. / Firjani, A.; Elnakib, A.; Khalifa, F. et al.
2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11. 2011. p. 1476-1479 5872679 (Proceedings - International Symposium on Biomedical Imaging).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

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AU - Firjani, A.

AU - Elnakib, A.

AU - Khalifa, F.

AU - Gimel'Farb, G.

AU - Abo El-Ghar, M.

AU - Suri, J.

AU - Elmaghraby, A.

AU - El-Baz, A.

PY - 2011

Y1 - 2011

N2 - Prostate segmentation is an essential step in developing any noninvasive Computer-Assisted Diagnostic (CAD) system for the early diagnosis of prostate cancer using Magnetic Resonance Images (MRI). In this paper, a novel framework for 3D segmentation of the prostate region from Dynamic Contrast Enhancement MRI (DCE-MRI) is proposed. The framework is based on Maximum A Posteriori (MAP) estimate of a new log-likelihood function that consists of : (i) 1 st-order visual appearance descriptors of the DCE-MRI, (ii) a 3D spatially rotation-variant 2nd-order homogeneity descriptor, and (iii) a 3D prostate shape descriptor. The shape prior is learned from the co-aligned 3D segmented prostate data. The visual appearances of the object and its background are described with marginal gray-level distributions obtained by separating their mixture over prostate data. The spatial interactions between the prostate voxels are modeled by a 3D 2nd-order rotation-variant Markov-Gibbs Random Field (MGRF) of object/background labels with analytically estimated potentials. Experiments with in vivo prostate DCE-MRI confirm the robustness and accuracy of the proposed approach.

AB - Prostate segmentation is an essential step in developing any noninvasive Computer-Assisted Diagnostic (CAD) system for the early diagnosis of prostate cancer using Magnetic Resonance Images (MRI). In this paper, a novel framework for 3D segmentation of the prostate region from Dynamic Contrast Enhancement MRI (DCE-MRI) is proposed. The framework is based on Maximum A Posteriori (MAP) estimate of a new log-likelihood function that consists of : (i) 1 st-order visual appearance descriptors of the DCE-MRI, (ii) a 3D spatially rotation-variant 2nd-order homogeneity descriptor, and (iii) a 3D prostate shape descriptor. The shape prior is learned from the co-aligned 3D segmented prostate data. The visual appearances of the object and its background are described with marginal gray-level distributions obtained by separating their mixture over prostate data. The spatial interactions between the prostate voxels are modeled by a 3D 2nd-order rotation-variant Markov-Gibbs Random Field (MGRF) of object/background labels with analytically estimated potentials. Experiments with in vivo prostate DCE-MRI confirm the robustness and accuracy of the proposed approach.

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U2 - 10.1109/ISBI.2011.5872679

DO - 10.1109/ISBI.2011.5872679

M3 - Conference contribution

AN - SCOPUS:80055056277

SN - 9781424441280

T3 - Proceedings - International Symposium on Biomedical Imaging

SP - 1476

EP - 1479

BT - 2011 8th IEEE International Symposium on Biomedical Imaging

T2 - 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11

Y2 - 30 March 2011 through 2 April 2011

ER -

Firjani A, Elnakib A, Khalifa F, Gimel'Farb G, Abo El-Ghar M, Suri J et al. A new 3D automatic segmentation framework for accurate segmentation of prostate from DCE-MRI. In 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11. 2011. p. 1476-1479. 5872679. (Proceedings - International Symposium on Biomedical Imaging). doi: 10.1109/ISBI.2011.5872679

A new 3D automatic segmentation framework for accurate segmentation of prostate from DCE-MRI

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