Isosurface reconstruction with topology control

Extracting isosurfaces from volumetric datasets is an essential step for indirect volume rendering algorithms. For physically measured data, e.g. in medical imaging applications, one often introduces topological errors such as small handles that stem from measurement inaccuracy and cavities that are generated by tight folds of an organ. During isosurface extraction these measurement errors result in a surface whose genus is much higher than that of the actual surface. In many cases, however, the topological type of the object under consideration is known beforehand, e.g., the cortex of a human brain is always homeomorphic to a sphere. By using topology preserving morphological operators we can exploit this knowledge to gradually dilate an initial set of voxels with correct topology until it fits the target isosurface. This approach avoids the formation of handles and cavities and guarantees a topologically correct reconstruction of the object's surface.

[1]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.

[2]  Rangasami L. Kashyap,et al.  Building Skeleton Models via 3-D Medial Surface/Axis Thinning Algorithms , 1994, CVGIP Graph. Model. Image Process..

[3]  Christos Davatzikos,et al.  Using a deformable surface model to obtain a shape representation of the cortex , 1996, IEEE Trans. Medical Imaging.

[4]  R. Ho Algebraic Topology , 2022 .

[5]  Longin Jan Latecki,et al.  Digital Topology , 1994 .

[6]  W. A. Hanson,et al.  Interactive 3D segmentation of MRI and CT volumes using morphological operations. , 1992, Journal of computer assisted tomography.

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

[8]  Max A. Viergever,et al.  Automatic Morphology-Based Brain Segmentation (MBRASE) from MRI-T1 Data , 2000, NeuroImage.

[9]  Longin Jan Latecki,et al.  An Algorithm for a 3D Simplicity Test , 1996, Comput. Vis. Image Underst..

[10]  Gilles Bertrand,et al.  A Boolean characterization of three-dimensional simple points , 1996, Pattern Recognition Letters.

[11]  Jacques-Olivier Lachaud,et al.  Continuous Analogs of Digital Boundaries: A Topological Approach to Iso-Surfaces , 2000, Graph. Model..

[12]  Rainer Goebel,et al.  An Efficient Algorithm for Topologically Correct Segmentation of the Cortical Sheet in Anatomical MR Volumes , 2001, NeuroImage.

[13]  Bhabatosh Chanda,et al.  Topology preservation in 3D digital space , 1994, Pattern Recognit..

[14]  M. Torrens Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268 , 1990 .

[15]  Zoë J. Wood,et al.  Topological Noise Removal , 2001, Graphics Interface.

[16]  Grégoire Malandain,et al.  Fast Binary Image Processing Using Binary Decision Diagrams , 1998, Comput. Vis. Image Underst..

[17]  Anders M. Dale,et al.  Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex , 2001, IEEE Transactions on Medical Imaging.

[18]  Azriel Rosenfeld,et al.  Digital topology: Introduction and survey , 1989, Comput. Vis. Graph. Image Process..

[19]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[20]  Alan C. Evans,et al.  Automated 3-D Extraction of Inner and Outer Surfaces of Cerebral Cortex from MRI , 2000, NeuroImage.

[21]  Marc Levoy,et al.  A volumetric method for building complex models from range images , 1996, SIGGRAPH.