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 error rate (ER) reached 95.15 and 7.81% in low-quality PET images, and 83.10 and 21.08% in low-contrast CT images. The experimental results demonstrated that the proposed method is effective and robust for liver boundary detection.
journal_name
Australas Phys Eng Sci Medjournal_title
Australasian physical & engineering sciences in medicineauthors
Liu X,Yang J,Song S,Song H,Ai D,Zhu J,Jiang Y,Wang Ydoi
10.1007/s13246-018-0650-ysubject
Has Abstractpub_date
2018-06-01 00:00:00pages
495-505issue
2eissn
0158-9938issn
1879-5447pii
10.1007/s13246-018-0650-yjournal_volume
41pub_type
杂志文章abstract：:The sample entropy (SampEn) has been widely used to quantify the complexity of RR-interval time series. It is a fact that higher complexity, and hence, entropy is associated with the RR-interval time series of healthy subjects. But, SampEn suffers from the disadvantage that it assigns higher entropy to the randomized ...
journal_title：Australasian physical & engineering sciences in medicine
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journal_title：Australasian physical & engineering sciences in medicine
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journal_title：Australasian physical & engineering sciences in medicine
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journal_title：Australasian physical & engineering sciences in medicine
pub_type： 杂志文章
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journal_title：Australasian physical & engineering sciences in medicine
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journal_title：Australasian physical & engineering sciences in medicine
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journal_title：Australasian physical & engineering sciences in medicine
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journal_title：Australasian physical & engineering sciences in medicine
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journal_title：Australasian physical & engineering sciences in medicine
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journal_title：Australasian physical & engineering sciences in medicine
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journal_title：Australasian physical & engineering sciences in medicine
pub_type： 杂志文章
doi：
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journal_title：Australasian physical & engineering sciences in medicine
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journal_title：Australasian physical & engineering sciences in medicine
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journal_title：Australasian physical & engineering sciences in medicine
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journal_title：Australasian physical & engineering sciences in medicine
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journal_title：Australasian physical & engineering sciences in medicine
pub_type： 杂志文章
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journal_title：Australasian physical & engineering sciences in medicine
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journal_title：Australasian physical & engineering sciences in medicine
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journal_title：Australasian physical & engineering sciences in medicine
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