Segmentation of Brain Tumors in Multi-parametric MR Images via Robust Statistic Information Propagation

A method is presented to segment brain tumors in multiparametric MR images via robustly propagating reliable statistical tumor information which is extracted from training tumor images using a support vector machine (SVM) classification method. The propagation of reliable statistical tumor information is implemented using a graph theoretic approach to achieve tumor segmentation with local and global consistency. To limit information propagation between image voxels of different properties, image boundary information is used in conjunction with image intensity similarity and anatomical spatial proximity to define weights of graph edges. The proposed method has been applied to 3D multi-parametric MR images with tumors of different sizes and locations. Quantitative comparison results with state-of-the-art methods indicate that our method can achieve competitive tumor segmentation performance.

[1]  Lawrence O. Hall,et al.  Automatic segmentation of non-enhancing brain tumors in magnetic resonance images , 2001, Artif. Intell. Medicine.

[2]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[3]  Guido Gerig,et al.  Synthetic Ground Truth for Validation of Brain Tumor MRI Segmentation , 2005, MICCAI.

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

[5]  Lawrence O. Hall,et al.  Automatic tumor segmentation using knowledge-based techniques , 1998, IEEE Transactions on Medical Imaging.

[6]  Mark W. Schmidt,et al.  3D Variational Brain Tumor Segmentation using a High Dimensional Feature Set , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[7]  Guido Gerig,et al.  A brain tumor segmentation framework based on outlier detection , 2004, Medical Image Anal..

[8]  P. Rousseeuw,et al.  A fast algorithm for the minimum covariance determinant estimator , 1999 .

[9]  M. M. Ahmed,et al.  Segmentation of Brain MR Images for Tumor Extraction by Combining Kmeans Clustering and Perona-Malik Anisotropic Diffusion Model , 2008 .

[10]  Guido Gerig,et al.  Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005, 8th International Conference, Palm Springs, CA, USA, October 26-29, 2005, Proceedings, Part I , 2005, MICCAI.

[11]  Bernhard Schölkopf,et al.  Learning with Local and Global Consistency , 2003, NIPS.

[12]  Richard M. Leahy,et al.  An Optimal Graph Theoretic Approach to Data Clustering: Theory and Its Application to Image Segmentation , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Hong Yan,et al.  Computerized tumour boundary detection using a Hopfield neural network , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[14]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[15]  Bernhard Schölkopf,et al.  Cluster Kernels for Semi-Supervised Learning , 2002, NIPS.

[16]  Sim Heng Ong,et al.  Level-set segmentation of brain tumors using a threshold-based speed function , 2010, Image Vis. Comput..

[17]  R. Kikinis,et al.  Recognizing Deviations from Normalcy for Brain Tumor Segmentation , 2002, MICCAI.

[18]  Guillermo Sapiro,et al.  Robust anisotropic diffusion , 1998, IEEE Trans. Image Process..

[19]  Guido Gerig,et al.  Level-set evolution with region competition: automatic 3-D segmentation of brain tumors , 2002, Object recognition supported by user interaction for service robots.

[20]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.

[21]  Dewey Odhner,et al.  A system for brain tumor volume estimation via MR imaging and fuzzy connectedness. , 2005, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

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

[23]  Guido Gerig,et al.  Model-based brain and tumor segmentation , 2002, Object recognition supported by user interaction for service robots.

[24]  Stephen T. C. Wong,et al.  Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field , 2009, Comput. Medical Imaging Graph..

[25]  Ron Kikinis,et al.  Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002 , 2002, Lecture Notes in Computer Science.

[26]  R. Kikinis,et al.  Automated segmentation of MR images of brain tumors. , 2001, Radiology.

[27]  Shuo Li,et al.  A Statistical Overlap Prior for Variational Image Segmentation , 2009, International Journal of Computer Vision.

[28]  Christos Davatzikos,et al.  Computer-assisted Segmentation of White Matter Lesions in 3d Mr Images Using Support Vector Machine 1 , 2022 .

[29]  Guillermo Sapiro,et al.  Anisotropic diffusion of multivalued images with applications to color filtering , 1996, IEEE Trans. Image Process..

[30]  Barry T. Thomas,et al.  Using Neural Networks to Automatically Detect Brain Tumours in MR Images , 1997, Int. J. Neural Syst..