Affine Adaptive Filtering of CT Data

C.-F. Westin, J. Richolt, V. Moharir, R. Kikinis
Medical Image Analysis
Volume 4, Number 2, Pages 161-172
June, 2000

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Abstract

A novel method for resampling and enhancing image data using multidimensional adaptive filters is presented. The underlying issue that this paper addresses is segmentation of image structures that are close in size to the voxel geometry. Adaptive filtering is used to reduce both the effects of partial volume averaging by resampling the data to a lattice with higher sample density and to reduce the image noise level. Resampling is achieved by constructing filter sets that have subpixel offsets relative to the original sampling lattice. The filters are also frequency corrected for ansisotropic voxel dimensions. The shift and the voxel dimensions are described by an affine transform and provides a model for tuning the filter frequency functions. The method has been evaluated on CT data where the voxels are in general non cubic. The in-plane resolution in CT image volumes is often higher by a factor of 3-10 than the through-plane resolution. The method clearly shows an improvement over conventional resampling techniques such as cubic spline interpolation and sinc interpolation.

Adaptive filtering of CT data through the head. The middle row shows thresholding of the original data with two different threshold. The lower row shows the effect of thresholding the adaptively filtered data.

Reference

Westin CF, Richolt J, Moharir V, Kikinis R. Affine adaptive filtering of CT data. Medical Image Analysis 2000;4(2):161-172.

Bibtex entry

@article{westinMEDIA00,
  author         = {C.-F. Westin and J. Richolt and V. Moharir and R. Kikinis},
  title          = {Affine Adaptive Filtering of {CT} Data},                   
  journal        = {Medical Image Analysis},                                   
  year           = 2000,                                                       
  volume         = 4,                                                          
  number         = 2,                                                          
  pages          = {161--172},                                                 
  month          = {June},                                                     
  note           = {}
}

Grants

NIH P41-RR13218 (NAC), NIH R01-RR11747, NIH P01-CA67165, NIH R01-CA45743, CIMIT

Research areas

Tensor, Filtering, CT