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.
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