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 - 2011/10/11
Y1 - 2011/10/11
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.
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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
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 47
EP - 56
BT - Prostate Cancer Imaging
T2 - International Workshop on Prostate Cancer Imaging: Image Analysis and Image-Guided Interventions, Held in Conjunction with MICCAI 2011
Y2 - 22 September 2011 through 22 September 2011
ER -