Semi-supervised prostate cancer segmentation with multispectral MRI

Prostate cancer is one of the leading causes of cancer related death for men in the United States. Recently, multispectral magnetic resonance imaging (MRI) has emerged as a promising noninvasive method for the localization of prostate cancer alternative to transrectal ultrasound (TRUS). This paper develops a semi-supervised method for prostate cancer localization using multispectral MRI. Patient-specific contrast can be utilized in this method for improved performance. We also propose to use an anisotropic filtering scheme to suppress the noise in the images. Using multispectral MR images, we demonstrate the effectiveness of this algorithm by testing it on real data sets and compare it to the results of a fully-automated method as well as to the earlier results. Both visual and quantitative comparisons are provided, illlustrating the success of the proposed method.

[1]  Leo Grady,et al.  Random Walks for Image Segmentation , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Alan C. Bovik,et al.  Smoothing low-SNR molecular images via anisotropic median-diffusion , 2002, IEEE Transactions on Medical Imaging.

[3]  Bernhard Schölkopf,et al.  New Support Vector Algorithms , 2000, Neural Computation.

[4]  William Wells,et al.  Detection of prostate cancer by integration of line-scan diffusion, T2-mapping and T2-weighted magnetic resonance imaging; a multichannel statistical classifier. , 2003, Medical physics.

[5]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Leo Grady,et al.  Multilabel random walker image segmentation using prior models , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[7]  T. Stamey,et al.  The abnormal prostate: MR imaging at 1.5 T with histopathologic correlation. , 1987, Radiology.

[8]  H. Huisman,et al.  Prostate cancer localization with dynamic contrast-enhanced MR imaging and proton MR spectroscopic imaging. , 2006, Radiology.

[9]  Xin Liu,et al.  Prostate Cancer Segmentation With Simultaneous Estimation of Markov Random Field Parameters and Class , 2009, IEEE Transactions on Medical Imaging.

[10]  I. S. Yetik,et al.  Prostate cancer segmentation with multispectral MRI using cost-sensitive Conditional Random Fields , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[11]  Gareth Funka-Lea,et al.  Graph Cuts and Efficient N-D Image Segmentation , 2006, International Journal of Computer Vision.

[12]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[13]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[14]  Masoom A Haider,et al.  Combined T2-weighted and diffusion-weighted MRI for localization of prostate cancer. , 2007, AJR. American journal of roentgenology.