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 language | English (US) |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 47-56 |
| Number of pages | 10 |
| Volume | 6963 LNCS |
| DOIs | |
| State | Published - 2011 |
| Event | International Workshop on Prostate Cancer Imaging: Image Analysis and Image-Guided Interventions, Held in Conjunction with MICCAI 2011 - Toronto, ON, Canada Duration: Sep 22 2011 → Sep 22 2011 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 6963 LNCS |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | International Workshop on Prostate Cancer Imaging: Image Analysis and Image-Guided Interventions, Held in Conjunction with MICCAI 2011 |
|---|---|
| Country | Canada |
| City | Toronto, ON |
| Period | 9/22/11 → 9/22/11 |
Fingerprint
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
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 proceeding › Conference contribution
}
TY - GEN
T1 - Facilitating 3D spectroscopic imaging through automatic prostate localization in MR images using random walker segmentation initialized via boosted classifiers
AU - Khurd, Parmeshwar
AU - Grady, Leo
AU - Gajera, Kalpitkumar
AU - Diallo, Mamadou
AU - Gall, Peter
AU - Requardt, Martin
AU - Kiefer, Berthold
AU - Weiss, Clifford
AU - Kamen, Ali
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=80053480142&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80053480142&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-23944-1_5
DO - 10.1007/978-3-642-23944-1_5
M3 - Conference contribution
AN - SCOPUS:80053480142
SN - 9783642239434
VL - 6963 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 47
EP - 56
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -