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A New Algorithm of Electronic Cleansing for Weak Faecal-Tagging CT Colonography

  • Le Lu
  • Bing Jian
  • Dijia Wu
  • Matthias Wolf
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8184)

Abstract

CT Colonography (CTC) has emerged as a mainstream clinical practice of colonic cancer screening and diagnosis. One of the most critical problems is to increase compliance with CTC examinations via minimal bowel preparation (i.e., weak faecal-tagging), which nevertheless causes much lower signal-noise-ratio than conventional preparation.

In this paper, we present a new algorithm pipeline of electronically cleansing tagging materials in CTC under reduced oral contrast dose. Our method has the following steps: 1, robust structure parsing to generate a list of volume regions of interest (ROIs) of tagging material (avoiding bone erosion); 2, effectively locating local tagging-air (AT) transitional surface regions; 3, a novel discriminative-generative algorithm to learn the higher-order image appearance model in AT using 3D Markov Random Fields (MRF); 4, accurate probability density function based voxel labeling corresponding to semantic classes. Validated on 26 weak faecal-tagging CTC cases from 3 medical sites, our method yields better visualization clarity and readability compared with the previous approach [1]. The whole system computes efficiently (e.g., < 40 seconds for CT images of 512×512×1000 +).

Keywords

Gaussian Mixture Model Compute Tomography Colonography Markov Random Fields Compute Tomography Intensity Algorithm Pipeline 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Le Lu
    • 1
  • Bing Jian
    • 1
  • Dijia Wu
    • 1
  • Matthias Wolf
    • 1
  1. 1.Siemens Corporate ResearchSiemens Medical Solutions USAUSA

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