Difference between revisions of "2014 Summer Project Week:Image To Mesh Conversion for Brain MRI"
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==Project Description== | ==Project Description== | ||
| − | This project generates a tetrahedral mesh from an input labeled image. The method consists of two | + | This project generates a tetrahedral mesh from an input labeled image. The method consists of two components: |
| − | * Body Centric Cubic Mesh Generation | + | * Body Centric Cubic (BCC) Mesh Generation |
| − | This | + | This component generates a Body Centric Cubic (BCC) mesh from a labeled image. Initially the generated mesh is homogeneous, that means does not distinguish different tissues. |
| − | Later the | + | Later the component specifies which tissue each tetrahedron belongs to. Each tissue is capable of automatically adjusting its resolution based on its geometric complexity and the |
predefined subdivision criterion. | predefined subdivision criterion. | ||
| − | * Mesh Compression | + | * Mesh Compression (MC) |
| − | This | + | This component deforms an input tetrahedral mesh towards the boundaries of the input labeled image. Two point sets are extracted for the mesh deformation. The first (source point set) consists of the exterior and interior surface vertices of the input mesh. The second (target point set) consists of the exterior and interior surface edge points in the input labeled image. Then the input mesh is deformed by registering the source to the target point set using a Physics-Based Non-Rigid Registration method. |
<div style="margin: 20px;"> | <div style="margin: 20px;"> | ||
<div style="width: 27%; float: left; padding-right: 3%;"> | <div style="width: 27%; float: left; padding-right: 3%;"> | ||
<h3>Objective</h3> | <h3>Objective</h3> | ||
| − | * | + | * Develop a CLI Slicer module for the Body Centric Cubic (BCC) mesh component. |
| + | * Develop a CLI Slicer module for the Mesh Compression (MC) component. | ||
</div> | </div> | ||
<div style="width: 27%; float: left; padding-right: 3%;"> | <div style="width: 27%; float: left; padding-right: 3%;"> | ||
<h3>Approach, Plan</h3> | <h3>Approach, Plan</h3> | ||
| − | * | + | * Currently the BCC and MC modules support the generation of single-tissue meshes. In the future they will extend to support multi-tissue meshes. |
</div> | </div> | ||
<div style="width: 27%; float: left; padding-right: 3%;"> | <div style="width: 27%; float: left; padding-right: 3%;"> | ||
<h3>Progress</h3> | <h3>Progress</h3> | ||
| − | * | + | *The implemented modules are tested on various inputs. |
</div> | </div> | ||
</div> | </div> | ||
Revision as of 17:55, 17 June 2014
Home < 2014 Summer Project Week:Image To Mesh Conversion for Brain MRIProject Description
This project generates a tetrahedral mesh from an input labeled image. The method consists of two components:
- Body Centric Cubic (BCC) Mesh Generation
This component generates a Body Centric Cubic (BCC) mesh from a labeled image. Initially the generated mesh is homogeneous, that means does not distinguish different tissues. Later the component specifies which tissue each tetrahedron belongs to. Each tissue is capable of automatically adjusting its resolution based on its geometric complexity and the predefined subdivision criterion.
- Mesh Compression (MC)
This component deforms an input tetrahedral mesh towards the boundaries of the input labeled image. Two point sets are extracted for the mesh deformation. The first (source point set) consists of the exterior and interior surface vertices of the input mesh. The second (target point set) consists of the exterior and interior surface edge points in the input labeled image. Then the input mesh is deformed by registering the source to the target point set using a Physics-Based Non-Rigid Registration method.
Objective
- Develop a CLI Slicer module for the Body Centric Cubic (BCC) mesh component.
- Develop a CLI Slicer module for the Mesh Compression (MC) component.
Approach, Plan
- Currently the BCC and MC modules support the generation of single-tissue meshes. In the future they will extend to support multi-tissue meshes.
Progress
- The implemented modules are tested on various inputs.
