Difference between revisions of "2009 Summer Project Week Project Segmentation of Muscoskeletal Images"
| Line 35: | Line 35: | ||
<h3>Approach, Plan</h3> | <h3>Approach, Plan</h3> | ||
| − | We are working on understanding the capabilities of RegisterImage module in Slicer to apply to knee datasets. | + | We are working on understanding the capabilities of RegisterImage module in Slicer to apply image-to-image registration on knee datasets. We are implementing masking algorithms to isolate specific knee bones, and perform parameter exploration to evaluate the sensitivity of registered images to input parameters. We are exploring the feasibility of applying python ICP-based registration algorithms to directly morph a surface model to a target image geometry. |
Our goals for the project week are: | Our goals for the project week are: | ||
| − | * Perform and evaluate results from | + | * Implement an algorithm to acquire masked regions of interest from MR datasets. |
| − | parameter space exploration study of RegisterImages | + | * Perform and evaluate results from a parameter space exploration study of RegisterImages |
Batchmake module on knee dataset. | Batchmake module on knee dataset. | ||
* Resolve issues in building Python modules from | * Resolve issues in building Python modules from | ||
slicer source code. | slicer source code. | ||
| − | * | + | * Explore possibility of model-to-image registration using |
existing atlas (.vtk, .stl) to a target image | existing atlas (.vtk, .stl) to a target image | ||
using Python ICP Registration module. | using Python ICP Registration module. | ||
Revision as of 18:47, 17 June 2009
Home < 2009 Summer Project Week Project Segmentation of Muscoskeletal Images
Knee MRI Image
Filled Femur Label Map
EM Segmented Output
64 Affine Registered on 58
64 Pipeline Affine Registered on 58
64 Bspline Registered on 58
Key Investigators
- Stanford: Harish Doddi, Saikat Pal, Scott Delp
- Harvard: Ron Kikinis
- Steve Pieper, Isomics, Inc.
- Luis Ibanez, Kitware, Inc.
Objective
The aim of this project is to develop an automatic/semi-automatic methodology to convert whole body imaging datasets into three-dimensional models for neuromuscular biomechanics and finite element simulations.
Specific Aims
- Implement image-to-image registration and segmentation algorithms on knee MR datasets. Registration includes masking algorithms to isolate structures of interest (i.e. femur, patella, and tibia bones).
- Perform a parameter space exploration on masked MR datasets.
- Develop model-to-image registration technique to morph existing model atlas to specified MR image geometry.
Approach, Plan
We are working on understanding the capabilities of RegisterImage module in Slicer to apply image-to-image registration on knee datasets. We are implementing masking algorithms to isolate specific knee bones, and perform parameter exploration to evaluate the sensitivity of registered images to input parameters. We are exploring the feasibility of applying python ICP-based registration algorithms to directly morph a surface model to a target image geometry.
Our goals for the project week are:
- Implement an algorithm to acquire masked regions of interest from MR datasets.
- Perform and evaluate results from a parameter space exploration study of RegisterImages
Batchmake module on knee dataset.
- Resolve issues in building Python modules from
slicer source code.
- Explore possibility of model-to-image registration using
existing atlas (.vtk, .stl) to a target image using Python ICP Registration module.
Progress
