Topology-Preserving Tissue Classification of Magnetic Resonance Brain Images
- 2 April 2007
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Medical Imaging
- Vol. 26 (4), 487-496
- https://doi.org/10.1109/tmi.2007.893283
Abstract
This paper presents a new framework for multiple object segmentation in medical images that respects the topological properties and relationships of structures as given by a template. The technique, known as topology-preserving, anatomy-driven segmentation (TOADS), combines advantages of statistical tissue classification, topology-preserving fast marching methods, and image registration to enforce object-level relationships with little constraint over the geometry. When applied to the problem of brain segmentation, it directly provides a cortical surface with spherical topology while segmenting the main cerebral structures. Validation on simulated and real images characterises the performance of the algorithm with regard to noise, inhomogeneities, and anatomical variationsKeywords
This publication has 28 references indexed in Scilit:
- CRUISE: Cortical reconstruction using implicit surface evolutionNeuroImage, 2004
- Sub-Voxel Topology Control for Level-Set SurfacesComputer Graphics Forum, 2003
- Topology correction in brain cortex segmentation using a multiscale, graph-based algorithmIEEE Transactions on Medical Imaging, 2002
- Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithmIEEE Transactions on Medical Imaging, 2001
- Automated graph-based analysis and correction of cortical volume topologyIEEE Transactions on Medical Imaging, 2001
- Topological operators for grayscale image processingJournal of Electronic Imaging, 2001
- Automated model-based tissue classification of MR images of the brainIEEE Transactions on Medical Imaging, 1999
- Volumetric transformation of brain anatomyIEEE Transactions on Medical Imaging, 1997
- Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentationIEEE Transactions on Pattern Analysis and Machine Intelligence, 1996
- From 3D magnetic resonance images to structural representations of the cortex topography using topology preserving deformationsJournal of Mathematical Imaging and Vision, 1995