Interface Detection in DTMRI

L. O'Donnell, W. E. L. Grimson, C.-F. Westin
Seventh International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'04)
Pages 360-367
September, 2004

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Abstract

We present a new method for detecting the interface, or edge, structure present in diffusion MRI. Interface detection is an important first step for applications including segmentation and registration. Additionally, due to the higher dimensionality of tensor data, humans are visually unable to detect edges as easily as in scalar data, so edge detection has potential applications in diffusion tensor visualization. Our method employs the computer vision techniques of local structure filtering and normalized convolution. We detect the edges in the tensor field by calculating a generalized local structure tensor, based on the sum of the outer products of the gradients of the tensor components. The local structure tensor provides a rotationally invariant description of edge orientation, and its shape after local averaging describes the type of edge. We demonstrate the ability to detect not only edges caused by differences in tensor magnitude, but also edges between regions of different tensor shape. We demonstrate the method's performance on synthetic data, on major fiber tract boundaries, and in one gray matter region.

Trace of the local structure tensor at several levels in an axial DTI dataset. Before filtering, the data was masked with a rough segmentation of the brain. Dark regions inside the brain, however, are not from masking but rather are regions of low edge magnitude.

Reference

O'Donnell L, Grimson WEL, Westin CF. Interface detection in DTMRI. In Seventh International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'04), Lecture Notes in Computer Science. Rennes - Saint Malo, France, 2004;360-367.

Bibtex entry

@InProceedings{odonnellMICCAI04,
  author         = {L. O'Donnell and W. E. L. Grimson and C.-F. Westin},       
  title          = {Interface Detection in {DTMRI}},                           
  booktitle      = {Seventh International Conference on Medical Image Computing
                   and  Computer-Assisted Intervention (MICCAI'04)},           
  pages          = {360--367},                                                 
  year           = {2004},                                                     
  series         = {Lecture Notes in Computer Science},                        
  address        = {Rennes - Saint Malo, France},                              
  month          = {September}
}

Grants

NIH P41-RR13218 (NAC), NSF ERC-8810274

Research areas

DTMRI, Tensor

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