Multi-layer cube sampling for liver boundary detection in PET-CT images

Australasian Physical & Engineering Sciences in Medicine
Xinxin LiuYongtian Wang

Abstract

Liver metabolic information is considered as a crucial diagnostic marker for the diagnosis of fever of unknown origin, and liver recognition is the basis of automatic diagnosis of metabolic information extraction. However, the poor quality of PET and CT images is a challenge for information extraction and target recognition in PET-CT images. The existing detection method cannot meet the requirement of liver recognition in PET-CT images, which is the key problem in the big data analysis of PET-CT images. A novel texture feature descriptor called multi-layer cube sampling (MLCS) is developed for liver boundary detection in low-dose CT and PET images. The cube sampling feature is proposed for extracting more texture information, which uses a bi-centric voxel strategy. Neighbour voxels are divided into three regions by the centre voxel and the reference voxel in the histogram, and the voxel distribution information is statistically classified as texture feature. Multi-layer texture features are also used to improve the ability and adaptability of target recognition in volume data. The proposed feature is tested on the PET and CT images for liver boundary detection. For the liver in the volume data, mean detection rate (DR) and mean...Continue Reading

References

Sep 29, 2004·Medical Image Analysis·J-F ManginJ Régis
Sep 14, 2006·IEEE Transactions on Medical Imaging·João V B SoaresMichael J Cree
Jul 22, 2011·IEEE Transactions on Bio-medical Engineering·Seongjin ParkYeong-Gil Shin

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Methods Mentioned

BETA
feature extraction

Software Mentioned

LTriDP
Gabor
VGG
GLCM
Net
LBP

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