Facilitating 3D spectroscopic imaging through automatic prostate localization in MR images using random walker segmentation initialized via boosted classifiers

Parmeshwar Khurd, Leo Grady, Kalpitkumar Gajera, Mamadou Diallo, Peter Gall, Martin Requardt, Berthold Kiefer, Clifford Weiss, Ali Kamen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Magnetic resonance imaging (MRI) plays a key role in the diagnosis, staging and treatment monitoring for prostate cancer. Automatic prostate localization in T2-weighted MR images could facilitate labor-intensive cancer imaging techniques such as 3D chemical shift MR spectroscopic imaging as well as advanced analysis techniques for diagnosis and treatment monitoring. We present a novel method for automatic segmentation of the prostate gland in MR images. Accurate prostate segmentation in MR imagery poses unique challenges. These include large variations in prostate anatomy and disease, intensity inhomogeneities, and near-field artifacts induced by endorectal coils. Our system meets these challenges with two key components. First is the automatic center detection of the prostate with a boosted classifier trained on intensity-based multi-level Gaussian Mixture Model Expectation Maximization (GMM-EM) segmentations of the raw MR images. The second is the use of a shape model in conjunction with Multi-Label Random Walker (MLRW) to constrain the seeding process within MLRW. Our system has been validated on a large database of non-isotropic T2-TSE (Turbo Spin Echo) and isotropic T2-SPACE (Sampling Perfection with Application Optimized Contrasts) images.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages47-56
Number of pages10
Volume6963 LNCS
DOIs
StatePublished - 2011
EventInternational Workshop on Prostate Cancer Imaging: Image Analysis and Image-Guided Interventions, Held in Conjunction with MICCAI 2011 - Toronto, ON, Canada
Duration: Sep 22 2011Sep 22 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6963 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

OtherInternational Workshop on Prostate Cancer Imaging: Image Analysis and Image-Guided Interventions, Held in Conjunction with MICCAI 2011
CountryCanada
CityToronto, ON
Period9/22/119/22/11

Fingerprint

Labels
Classifiers
Segmentation
Classifier
Imaging
Imaging techniques
Monitoring
Chemical shift
Magnetic resonance imaging
Personnel
Sampling
Prostate Cancer
Expectation Maximization
Magnetic Resonance Imaging
Gaussian Mixture Model
Anatomy
Coil
Near-field
Inhomogeneity
Cancer

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Khurd, P., Grady, L., Gajera, K., Diallo, M., Gall, P., Requardt, M., ... Kamen, A. (2011). Facilitating 3D spectroscopic imaging through automatic prostate localization in MR images using random walker segmentation initialized via boosted classifiers. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6963 LNCS, pp. 47-56). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6963 LNCS). https://doi.org/10.1007/978-3-642-23944-1_5

Facilitating 3D spectroscopic imaging through automatic prostate localization in MR images using random walker segmentation initialized via boosted classifiers. / Khurd, Parmeshwar; Grady, Leo; Gajera, Kalpitkumar; Diallo, Mamadou; Gall, Peter; Requardt, Martin; Kiefer, Berthold; Weiss, Clifford; Kamen, Ali.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6963 LNCS 2011. p. 47-56 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6963 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Khurd, P, Grady, L, Gajera, K, Diallo, M, Gall, P, Requardt, M, Kiefer, B, Weiss, C & Kamen, A 2011, Facilitating 3D spectroscopic imaging through automatic prostate localization in MR images using random walker segmentation initialized via boosted classifiers. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 6963 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6963 LNCS, pp. 47-56, International Workshop on Prostate Cancer Imaging: Image Analysis and Image-Guided Interventions, Held in Conjunction with MICCAI 2011, Toronto, ON, Canada, 9/22/11. https://doi.org/10.1007/978-3-642-23944-1_5
Khurd P, Grady L, Gajera K, Diallo M, Gall P, Requardt M et al. Facilitating 3D spectroscopic imaging through automatic prostate localization in MR images using random walker segmentation initialized via boosted classifiers. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6963 LNCS. 2011. p. 47-56. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-23944-1_5
Khurd, Parmeshwar ; Grady, Leo ; Gajera, Kalpitkumar ; Diallo, Mamadou ; Gall, Peter ; Requardt, Martin ; Kiefer, Berthold ; Weiss, Clifford ; Kamen, Ali. / Facilitating 3D spectroscopic imaging through automatic prostate localization in MR images using random walker segmentation initialized via boosted classifiers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6963 LNCS 2011. pp. 47-56 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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