Difference between revisions of "2011 Winter Project Week:MeshCurvolver"

From NAMIC Wiki
Jump to: navigation, search
(Created page with '__NOTOC__ <gallery> Image:PW-SLC2010.png|Projects List Image:BrainH.png|Surface Mean Curvature Image:Sulci6_done2.png|Converged Contour Surr…')
 
Line 7: Line 7:
 
Image:bone-end3.png|Segmenting Tibia Fracture via Surface Geometry
 
Image:bone-end3.png|Segmenting Tibia Fracture via Surface Geometry
 
Image:Surface_H_c.png|Surface curvature display for segmented colon MRI in Slicer
 
Image:Surface_H_c.png|Surface curvature display for segmented colon MRI in Slicer
Image:lsvtk_qtcreator.png|'Extreme Programming' with QT Creator in Ubuntu Linux
+
Image:KSlice1.png|Femur Surface Segmentation
 +
Image:constraint_example_pts.png|User-Constraint Input Points in a Slice
 
</gallery>
 
</gallery>
  
 
== Key Investigators ==
 
== Key Investigators ==
* Georgia Tech: Peter Karasev, Karol Chudy, Allen Tannenbaum
+
* Georgia Tech: Peter Karasev, Ivan Kolesov, Karol Chudy, Allen Tannenbaum
 
* BWH: Ron Kikinis
 
* BWH: Ron Kikinis
  
Line 18: Line 19:
  
 
<h3>Objective</h3>
 
<h3>Objective</h3>
We are developing methods for segmentation of surfaces defined by polygonal meshes. In image or volume-slice segmentation, 'information' comes from image intensity, while in mesh surface segmentation the information is purely geometric. The goal of this project is to develop algorithms allowing a user to quickly and robustly segment one or several regions on a surface. Specifically, it is required to reconcile some concepts in classical differential geometry and embedded curves with the numerical and discrete-space constraints of polygon meshes.  
+
We are developing methods for segmentation of surfaces defined by polygonal meshes. In image or volume-slice segmentation, 'information' comes from image intensity, while in mesh surface segmentation the information is purely geometric. The goal of this project is to develop algorithms allowing a user to quickly and robustly segment one or several regions on a surface.  
  
 +
Ill-posed mesh data can be avoided by keeping access to the volumetric data to resolve complex regions. "User-Input-Constraints" can be incrementally updated to improve the surface model when needed.
  
 
</div>
 
</div>
Line 35: Line 37:
  
 
Extend Functionality in VTK and ITK to perform needed operations
 
Extend Functionality in VTK and ITK to perform needed operations
 
The module pipeline now contains three classes that extend vtkPolyDataAlgorithm. It now makes use of vtkSmartPointer.
 
  
 
* vtkInitClosedPath : take in poly data and set of fiducial points, create a closed path including the closest points on the mesh to the given fiducials. Searches for pre-existence of geometry information, pass-through if it already exists.
 
* vtkInitClosedPath : take in poly data and set of fiducial points, create a closed path including the closest points on the mesh to the given fiducials. Searches for pre-existence of geometry information, pass-through if it already exists.
Line 43: Line 43:
  
 
* vtkLevelSetMeshEvolver : take in poly data with a scalar array defining the current region boundaries. Update the boundaries with curve evolution and return poly data with updated scalar array.
 
* vtkLevelSetMeshEvolver : take in poly data with a scalar array defining the current region boundaries. Update the boundaries with curve evolution and return poly data with updated scalar array.
 +
 +
* added standalone executable to append surface curvature as a scalar map in vtk poly data
 +
 +
* added GUI program for algorithm development; maintains the volume data, label maps, constraint inputs, and output surface to avoid excess re-computation.
 +
 +
* need to port the mixed  matlab/mex/C "SPGL1" package to pure C to solve for sparse solutions to underdetermined systems (generating the surface models)
  
 
</div>
 
</div>

Revision as of 18:55, 10 January 2011

Home < 2011 Winter Project Week:MeshCurvolver

Key Investigators

  • Georgia Tech: Peter Karasev, Ivan Kolesov, Karol Chudy, Allen Tannenbaum
  • BWH: Ron Kikinis

Objective

We are developing methods for segmentation of surfaces defined by polygonal meshes. In image or volume-slice segmentation, 'information' comes from image intensity, while in mesh surface segmentation the information is purely geometric. The goal of this project is to develop algorithms allowing a user to quickly and robustly segment one or several regions on a surface.

Ill-posed mesh data can be avoided by keeping access to the volumetric data to resolve complex regions. "User-Input-Constraints" can be incrementally updated to improve the surface model when needed.

Approach, Plan

Surface Geometry Computation

Closest Path Between Initial Points

Fast Level Set Implementation for Unstructured Mesh

Support 'global geometry' estimation for statistical analysis versus local-only for fast segmentation

Extend Functionality in VTK and ITK to perform needed operations

  • vtkInitClosedPath : take in poly data and set of fiducial points, create a closed path including the closest points on the mesh to the given fiducials. Searches for pre-existence of geometry information, pass-through if it already exists.
  • vtkComputeLocalGeometry : take in poly data, compute curvature, its derivatives, and related differential geometric quantities. Uses its own version of curvature computation- vtk versions are not useable because they only consider 1-neighborhood. Searches for pre-existence of geometry information, pass-through if it already exists.
  • vtkLevelSetMeshEvolver : take in poly data with a scalar array defining the current region boundaries. Update the boundaries with curve evolution and return poly data with updated scalar array.
  • added standalone executable to append surface curvature as a scalar map in vtk poly data
  • added GUI program for algorithm development; maintains the volume data, label maps, constraint inputs, and output surface to avoid excess re-computation.
  • need to port the mixed matlab/mex/C "SPGL1" package to pure C to solve for sparse solutions to underdetermined systems (generating the surface models)


References

P.A. Karasev, J.G. Malcolm, M. Niethammer, R. Kikinis, A. Tannenbaum. User-Driven 3D Mesh Region Targeting. SPIE Medical Imaging 2010.