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Paediatric Liver Segmentation for Low-Contrast CT Images

  • Mariusz Bajger
  • Gobert Lee
  • Martin Caon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11076)

Abstract

CT images from combined PET-CT scanners are of low contrast. Automatic organ segmentation on these images are challenging. This paper proposed an adaptive kernel-based Statistical Region Merging (SRM) algorithm for paediatric liver segmentation in low contrast PET-CT images. The results are compared to that from the original SRM. The average dice index is 0.79 for SRM and 0.85 for the adaptive kernel-based SRM. In addition, the proposed method was successful in segmenting all 37 CT images while SRM failed in 5 images.

Keywords

Low contrast CT PET-CT Adaptive-kernel 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Medical Device Research InstituteFlinders UniversityAdelaideAustralia
  2. 2.College of Science and EngineeringFlinders UniversityAdelaideAustralia
  3. 3.College of Nursing and Health SciencesFlinders UniversityAdelaideAustralia

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