Name
Title | ||
|---|---|---|
DTI-DeformIt: Generating ground-truth validation data for diffusion tensor image analysis tasks |
Abstract | ||
|---|---|---|
We propose DTI-DeformIt: a framework to generate realistic synthetic datasets from a smaller number of, or even one, annotated image(s). Our approach extends the DeformIt technique of Hamarneh et al. [1] to handle the deformations and noise conditions of diffusion tensor images. An implementation of our proposed framework is also provided as a free download. We further show that DTI-DeformIt generates images that, according to eigenvector distance, are no different from real images than other real images, making them suitable for machine learning and validation. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ISBI.2014.6867974 | Biomedical Imaging |
Keywords | Field | DocType |
biodiffusion,biomedical MRI,deformation,eigenvalues and eigenfunctions,learning (artificial intelligence),medical image processing,deformations,diffusion tensor image analysis tasks,eigenvector distance,machine learning,noise conditions,Diffusion Tensor Imaging,Image Generation,Machine Learning,Validation | Computer vision,Diffusion MRI,Pattern recognition,Computer science,Ground truth,Artificial intelligence,Structure tensor,Real image,Eigenvalues and eigenvectors | Conference |
ISSN | Citations | PageRank |
1945-7928 | 0 | 0.34 |
References | Authors | |
12 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Brian G. Booth | 1 | 88 | 7.30 |
| Ghassan Hamarneh | 2 | 1353 | 110.14 |