Automatic segmentation of ground-glass opacities in lung CT images by using Markov random field-based algorithms

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Application of the 3D slicer chest imaging platform segmentation algorithm for large lung nodule delineation Segmentation of lung lesions on CT scans using watershed, active contours, and Markov random field.

P2860

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P2860

Automatic segmentation of ground-glass opacities in lung CT images by using Markov random field-based algorithms

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http://www.wikidata.org/entity/Q35948943

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2012 nî lūn-bûn

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2012年の論文

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2012年論文

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2012年論文

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2012年論文

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2012年論文

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2012年論文

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2012年论文

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2012年论文

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2012年论文

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name

Automatic segmentation of grou ...... random field-based algorithms

@ast

Automatic segmentation of grou ...... random field-based algorithms

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type

Item

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Automatic segmentation of grou ...... random field-based algorithms

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Automatic segmentation of grou ...... random field-based algorithms

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Automatic segmentation of grou ...... random field-based algorithms

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Automatic segmentation of grou ...... random field-based algorithms

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P1476

Automatic segmentation of grou ...... random field-based algorithms

@en

P2093

Guozhen Zhang

http://www.w3.org/2001/XMLSchema#string

Jianguo Zhang

http://www.w3.org/2001/XMLSchema#string

Yanjie Zhu

http://www.w3.org/2001/XMLSchema#string

Yanqing Hua

http://www.w3.org/2001/XMLSchema#string

Yongqing Tan

http://www.w3.org/2001/XMLSchema#string

P2860

Feature selection and performance evaluation of support vector machine (SVM)-based classifier for differentiating benign and malignant pulmonary nodules by computed tomography.

Computer aided characterization of the solitary pulmonary nodule using volumetric and contrast enhancement features.

Texture segmentation using Gaussian-Markov random fields and neural oscillator networks.

Markov random field segmentation of brain MR images.

Computerized analysis of the likelihood of malignancy in solitary pulmonary nodules with use of artificial neural networks.

CT screening for lung cancer: frequency and significance of part-solid and nonsolid nodules.

Sonar image segmentation using an unsupervised hierarchical MRF model.

Usefulness of an artificial neural network for differentiating benign from malignant pulmonary nodules on high-resolution CT: evaluation with receiver operating characteristic analysis.

Automatic detection and quantification of ground-glass opacities on high-resolution CT using multiple neural networks: comparison with a density mask.

Texture classification and segmentation using wavelet frames.

P2888

s10278-011-9435-5

P304

409-422

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scholarly article

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10.1007/S10278-011-9435-5

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2012-06-01T00:00:00Z

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51804420

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22089834

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P921

automation

ground glass opacity

P932

3348983

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Automatic segmentation of ground-glass opacities in lung CT images by using Markov random field-based algorithms

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P2860

P2860

Automatic segmentation of ground-glass opacities in lung CT images by using Markov random field-based algorithms

description

name

type

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P1433

P1476

P2093

P2860

P2888

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P31

P356

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P478

P577

P5875

P698

P921

P932

P356

P1433

P1476

P2093

P2860

P2888

P304

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P356

P433

P478

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P5875

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P921

P932