Topology Preserving Tissue Classification with Fast Marching and Topology Templates

This paper presents a novel approach for object segmentation in medical images that respects the topological relationships of multiple structures as given by a template. The algorithm combines advantages of tissue classification, digital topology, and level-set evolution into a topology-invariant multiple-object fast marching method. The technique can handle any given topology and enforces object-level relationships with little constraint over the geometry. Applied to brain segmentation, it sucessfully extracts gray matter and white matter structures with the correct spherical topology without topology correction or editing of the subcortical structures.

[1]  Koenraad Van Leemput,et al.  Automated model-based tissue classification of MR images of the brain , 1999, IEEE Transactions on Medical Imaging.

[2]  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.

[3]  Isabelle Bloch,et al.  From 3D magnetic resonance images to structural representations of the cortex topography using topology preserving deformations , 1995, Journal of Mathematical Imaging and Vision.

[4]  Rachid Deriche,et al.  Implicit Active Shape Models for 3D Segmentation in MR Imaging , 2004, MICCAI.

[5]  Pierre Hellier,et al.  Level Set Methods in an EM Framework for Shape Classification and Estimation , 2004, International Conference on Medical Image Computing and Computer-Assisted Intervention.

[6]  Ronald Fedkiw,et al.  Level set methods and dynamic implicit surfaces , 2002, Applied mathematical sciences.

[7]  Leif Kobbelt,et al.  Sub‐Voxel Topology Control for Level‐Set Surfaces , 2003, Comput. Graph. Forum.

[8]  Terry M. Peters,et al.  Medical Image Computing and Computer-Assisted Intervention - MICCAI 2003 , 2003, Lecture Notes in Computer Science.

[9]  Pierre-Louis Bazin,et al.  Free software tools for atlas-based volumetric neuroimage analysis , 2005, SPIE Medical Imaging.

[10]  Gilles Bertrand,et al.  Topological operators for grayscale image processing , 2001, J. Electronic Imaging.

[11]  Richard M. Leahy,et al.  Automated graph-based analysis and correction of cortical volume topology , 2001, IEEE Transactions on Medical Imaging.

[12]  Xiao Han,et al.  A Topology Preserving Level Set Method for Geometric Deformable Models , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Tony F. Chan,et al.  A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model , 2002, International Journal of Computer Vision.

[15]  Bernd Hamann,et al.  Detecting Critical Regions in Scalar Fields , 2003, VisSym.

[16]  W. Eric L. Grimson,et al.  Topological Correction of Subcortical Segmentation , 2003, MICCAI.

[17]  R. Leahy,et al.  Magnetic Resonance Image Tissue Classification Using a Partial Volume Model , 2001, NeuroImage.

[18]  Michael I. Miller,et al.  Volumetric transformation of brain anatomy , 1997, IEEE Transactions on Medical Imaging.

[19]  Benoit M. Dawant,et al.  The adaptive bases algorithm for intensity-based nonrigid image registration , 2003, IEEE Transactions on Medical Imaging.

[20]  Jerry L. Prince,et al.  Mapping Techniques for Aligning Sulci across Multiple Brains , 2003, MICCAI.

[21]  Pierre-Louis Bazin,et al.  Topology Smoothing for Segmentation and Surface Reconstruction , 2004, MICCAI.

[22]  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.

[23]  Matthew J. McAuliffe,et al.  Medical Image Processing, Analysis and Visualization in clinical research , 2001, Proceedings 14th IEEE Symposium on Computer-Based Medical Systems. CBMS 2001.

[24]  James A. Sethian,et al.  Level Set Methods and Fast Marching Methods , 1999 .

[25]  Jerry L. Prince,et al.  Topology correction in brain cortex segmentation using a multiscale, graph-based algorithm , 2002, IEEE Transactions on Medical Imaging.

[26]  Xiao Han,et al.  CRUISE: Cortical reconstruction using implicit surface evolution , 2004, NeuroImage.

[27]  Dzung L. Pham,et al.  Spatial Models for Fuzzy Clustering , 2001, Comput. Vis. Image Underst..