Prostate cancer localization with multispectral MRI based on relevance vector machines

Authors:
S. Ozer;M. A. Haider;D. L. Langer;T. H. van der Kwast;A. J. Evans;M. N. Wernick;J. Trachtenberg;I. S. Yetik
Affiliations:
Medical Imaging Research Center, Electrical & Computer Engineering Dept., Illinois Institute of Technology, Chicago, IL;Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network and Mount Sinai Hospital and Institute of Medical Science, University of Toronto, King's College, Toronto ...;Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network and Mount Sinai Hospital and Institute of Medical Science, University of Toronto, King's College, Toronto ...;Department of Pathology and Laboratory Medicine, Toronto General Hospital, Toronto, Ontario, Canada;Department of Pathology and Laboratory Medicine, Toronto General Hospital, Toronto, Ontario, Canada;Medical Imaging Research Center, Electrical & Computer Engineering Dept., Illinois Institute of Technology, Chicago, IL;Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network and Mount Sinai Hospital, Toronto, Ontario, Canada;Medical Imaging Research Center, Electrical & Computer Engineering Dept., Illinois Institute of Technology, Chicago, IL
Venue:
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Year:
2009

Citing 2
Cited 3

Sparse bayesian learning and the relevance vector machine

The Journal of Machine Learning Research
Sparse Bayesian Learning for Efficient Visual Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence

Using relative contrast and iterative normalization for improved prostate cancer localization with multispectral MRI

ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Using K-NN SVMs for performance improvement and comparison to K-highest lagrange multipliers selection

SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
A Two Stage Sequential Ensemble Applied to the Classification of Alzheimer's Disease Based on MRI Features

Neural Processing Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

Prostate cancer is one of the leading causes of cancer death for men. However, early detection before cancer spreads beyond the prostate can reduce the mortality. Therefore, invivo imaging techniques play an important role to localize the prostate cancer for treatment. Although Magnetic Resonance Imaging (MRI) has been proposed to localize prostate cancer, the studies on automated localization with multispectral MRI have been limited. In this study we propose combining the pharmacokinetic parameters derived from DCE MRI with T2 MRI and DWI. We also propose to use Relevance Vector Machines (RVM) for automatic prostate cancer localization, compare its performance to Support Vector Machines (SVM) and show that RVM can produce more accurate and more efficient segmentation results than SVM for automated prostate cancer localization with multispectral MRI.