Automatic Segmentation of Lung Carcinoma Using 3D Texture Features in 18-FDG PET/CT.
about
A review on segmentation of positron emission tomography imagesComputer-assisted delineation of lung tumor regions in treatment planning CT images with PET/CT image sets based on an optimum contour selection methodA framework based on Hidden Markov Trees for multimodal PET/CT image co-segmentation.Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211.Intra-tumour 18F-FDG uptake heterogeneity decreases the reliability on target volume definition with positron emission tomography/computed tomography imaging.Texture classification using feature selection and kernel-based techniques
P2860
Automatic Segmentation of Lung Carcinoma Using 3D Texture Features in 18-FDG PET/CT.
description
2013 nî lūn-bûn
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2013年の論文
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name
Automatic Segmentation of Lung Carcinoma Using 3D Texture Features in 18-FDG PET/CT.
@en
type
label
Automatic Segmentation of Lung Carcinoma Using 3D Texture Features in 18-FDG PET/CT.
@en
prefLabel
Automatic Segmentation of Lung Carcinoma Using 3D Texture Features in 18-FDG PET/CT.
@en
P2093
P2860
P356
P1476
Automatic Segmentation of Lung Carcinoma Using 3D Texture Features in 18-FDG PET/CT.
@en
P2093
Alexander Sun
Curtis Caldwell
Daniel Markel
Hamideh Alasti
Hany Soliman
Justin Lee
P2860
P304
P356
10.1155/2013/980769
P577
2013-02-26T00:00:00Z