Anatomical guided segmentation with non-stationary tissue class distributions in an expectation-maximization framework

High quality segmentation of brain MR images is a challenging task. To deal with this problem many automatic segmentation methods rely on atlas information of anatomical structures. We will further investigate this line of research by introducing hierarchical representations of anatomical structures in an expectation-maximization like framework. This new approach enables us to divide a complex segmentation scenario into less difficult sub-problems reducing the scenario's statistical complexity. We will demonstrate the method's strength by segmenting a set of brain MR images into 31 different anatomical structures as well as comparing it to other methods.

[1]  D. Louis Collins,et al.  ANIMAL+INSECT: Improved Cortical Structure Segmentation , 1999, IPMI.

[2]  Jerry L Prince,et al.  Current methods in medical image segmentation. , 2000, Annual review of biomedical engineering.

[3]  O. Faugeras,et al.  Statistical shape influence in geodesic active contours , 2002, 5th IEEE EMBS International Summer School on Biomedical Imaging, 2002..

[4]  James S. Duncan,et al.  Statistical Neighbor Distance Influence in Active Contours , 2002, MICCAI.

[5]  A. Dale,et al.  Whole Brain Segmentation Automated Labeling of Neuroanatomical Structures in the Human Brain , 2002, Neuron.

[6]  W. Eric L. Grimson,et al.  A shape-based approach to the segmentation of medical imagery using level sets , 2003, IEEE Transactions on Medical Imaging.

[7]  W. Eric L. Grimson,et al.  Adaptive Segmentation of MRI Data , 1995, CVRMed.

[8]  Baba C. Vemuri,et al.  An Accurate and Efficient Bayesian Method for Automatic Segmentation of Brain MRI , 2002, ECCV.

[9]  W. Eric L. Grimson,et al.  Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images , 2002, MICCAI.

[10]  William M. Wells,et al.  Validation of Image Segmentation and Expert Quality with an Expectation-Maximization Algorithm , 2002, MICCAI.

[11]  Ron Kikinis,et al.  Improved watershed transform for medical image segmentation using prior information , 2004, IEEE Transactions on Medical Imaging.

[12]  Ron Kikinis,et al.  Adaptive Template Moderated Brain Tumor Segmentation in MRI , 1999, Bildverarbeitung für die Medizin.

[13]  Nicholas Ayache,et al.  Three-dimensional multimodal brain warping using the Demons algorithm and adaptive intensity corrections , 2001, IEEE Transactions on Medical Imaging.

[14]  Koenraad Van Leemput,et al.  Automated model-based bias field correction of MR images of the brain , 1999, IEEE Transactions on Medical Imaging.

[15]  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.