Semi-automatic Discrimination of Normal Tissue and Liver Cancer Lesions in Contrast Enhanced X-Ray CT-Scans

  • Sanat Upadhyay
  • Manos Papadakis
  • Saurabh Jain
  • Gregory Gladish M.D.
  • Ioannis A. Kakadiaris
  • Robert Azencott
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7601)


In this paper we present a set of 3D-rigid motion invariant texture features. We experimentally establish that when they are combined with mean attenuation intensity differences the new augmented features are capable of discriminating normal from abnormal liver tissue in arterial phase contrast enhanced X-ray CT–scans with high sensitivity and specificity. To extract these features CT-scans are processed in their native dimensionality. We experimentally observe that the 3D-rotational invariance of the proposed features improves the clustering of the feature vectors extracted from normal liver tissue samples.


Liver cancer 3D-texture classification rotationally invariant features soft tissue discrimination 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Sanat Upadhyay
    • 1
  • Manos Papadakis
    • 1
  • Saurabh Jain
    • 2
  • Gregory Gladish M.D.
    • 3
  • Ioannis A. Kakadiaris
    • 4
  • Robert Azencott
    • 1
  1. 1.Department of MathematicsUniversity of HoustonUSA
  2. 2.Center for Imaging SciencesJohn Hopkins UniversityUSA
  3. 3.MD Anderson Cancer CenterThe University of TexasUSA
  4. 4.Department of Computer ScienceUniversity of HoustonUSA

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