Classifying ct image data into material fractions by a scale and rotation invariant edge model

A fully automated method is presented to classify 3-D CT data into material fractions. An analytical scale-invariant description relating the data value to derivatives around Gaussian blurred step edges-arch model-is applied to uniquely combine robustness to noise, global signal fluctuations, anisotropic scale, noncubic voxels, and ease of use via a straightforward segmentation of 3-D CT images through material fractions. Projection of noisy data value and derivatives onto the arch yields a robust alternative to the standard computed Gaussian derivatives. This results in a superior precision of the method. The arch-model parameters are derived from a small, but over-determined, set of measurements (data values and derivatives) along a path following the gradient uphill and downhill starting at an edge voxel. The model is first used to identify the expected values of the two pure materials (named and) and thereby classify the boundary. Second, the model is used to approximate the underlying noise-free material fractions for each noisy measurement. An iso-surface of constant material fraction accurately delineates the ma-terial boundary in the presence of noise and global signal fluctuations. This approach enables straightforward segmentation of 3-D CT images into objects of interest for computer-aided diagnosis and offers an easy tool for the design of otherwise compli-cated transfer functions in high-quality visualizations. The method is applied to segment a tooth volume for visualization and digital cleansing for virtual colonoscopy.

@Article { SVTPV07a,
  author       = "Serlie, I.W.O. and Vos, F.M. and Truyen, R. and Post, Frits H. and Vliet, L.J. van",
  title        = "Classifying ct image data into material fractions by a scale and rotation invariant edge model",
  journal      = "Image Processing, IEEE Transactions on",
  number       = "12",
  volume       = "16",
  year         = "2007",
  note         = "1057-7149",
  keywords     = "Gaussian processes, computerised tomography, edge detection, image classification, image segmentation, medical image processingCT image data classification, Gaussian blurred step edges, computer-aided diagnosis, image segmentation, material fractions, noi",
  url          = "http://graphics.tudelft.nl/Publications-new/2007/SVTPV07a"
}