Tissue Classification Based on 3D Local Intensity Structure for Volume Rendering

Y. Sato, C.-F. Westin, A. Bhalerao, S. Nakajima, N. Shiraga, S. Tamura, R. Kikinis
IEEE Trans on Visualization and Computer Graphics
Volume 6, Number 2, Pages 160-180
2000

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Abstract

classification using three-dimensional (3D) derivative features in the volume rendering pipeline. In conventional tissue classification for a scalar volume, tissues of interest are characterized by an opacity transfer function defined as a one-dimensional (1D) function of the original volume intensity. To overcome the limitations inherent in conventional 1D opacity functions, we propose a tissue classification method that employs a multidimensional opacity function, which is a function of the 3D derivative features calculated from a scalar volume as well as the volume intensity. Tissues of interest are characterized by explicitly defined classification rules based on 3D filter responses highlighting local structures, such as edge, sheet, line, and blob, which typically correspond to tissue boundaries, cortices, vessels, and nodules, respectively, in medical volume data. The 3D local structure filters are formulated using the gradient vector and Hessian matrix of the volume intensity function combined with isotropic Gaussian blurring. These filter responses and the original intensity define a multidimensional feature space in which multichannel tissue classification strategies are designed. The usefulness of the proposed method is demonstrated by comparisons with conventional single-channel classification using both synthesized data and clinical data acquired with CT (computed tomography) and MRI (magnetic resonance imaging) scanners. The improvement in image quality obtained using multichannel classification is confirmed by evaluating the contrast and contrast-to-noise ratio in the resultant volume-rendered images with variable opacity values.

Reference

Sato Y, Westin CF, Bhalerao A, Nakajima S, Shiraga N, Tamura S, Kikinis R. Tissue classification based on 3d local intensity structure for volume rendering. IEEE Trans on Visualization and Computer Graphics 2000;6(2):160-180.

Bibtex entry

@Article{satoTVCG00,
  author         = {Y. Sato and C.-F. Westin and A. Bhalerao and S. Nakajima   
                   and  N. Shiraga and S. Tamura and R. Kikinis},              
  title          = {Tissue Classification Based on 3D Local Intensity Structure
                   for  Volume Rendering},                                     
  journal        = {IEEE Trans on Visualization and Computer Graphics},        
  year           = 2000,                                                       
  volume         = 6,                                                          
  number         = 2,                                                          
  pages          = {160--180}
}

Grants

NIH P01-CA67165

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