Literature DB >> 2326513

Quantitative computer-aided analysis of lung texture in chest radiographs.

S Katsuragawa1, K Doi, H MacMahon, N Nakamori, Y Sasaki, J J Fennessy.   

Abstract

The authors describe a computerized method to quantify and characterize interstitial diseases by using physical texture measures obtained from an analysis of the power spectrum of lung textures in digital chest radiographs. They compared these texture measures obtained from standard radiographs from the International Labour Office (ILO) classification scheme and the ILO classification categories for small opacities in pneumoconioses. Their preliminary results indicate that texture measures obtained from this computer analysis of the ILO standard radiographs correspond closely with the ILO classification categories.

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Year:  1990        PMID: 2326513     DOI: 10.1148/radiographics.10.2.2326513

Source DB:  PubMed          Journal:  Radiographics        ISSN: 0271-5333            Impact factor:   5.333


  10 in total

1.  Computerized analysis of abnormal asymmetry in digital chest radiographs: evaluation of potential utility.

Authors:  S G Armato; M L Giger; H MacMahon
Journal:  J Digit Imaging       Date:  1999-02       Impact factor: 4.056

2.  Computerized analysis of pneumoconiosis in digital chest radiography: effect of artificial neural network trained with power spectra.

Authors:  Eiichiro Okumura; Ikuo Kawashita; Takayuki Ishida
Journal:  J Digit Imaging       Date:  2011-12       Impact factor: 4.056

3.  Computerized Classification of Pneumoconiosis on Digital Chest Radiography Artificial Neural Network with Three Stages.

Authors:  Eiichiro Okumura; Ikuo Kawashita; Takayuki Ishida
Journal:  J Digit Imaging       Date:  2017-08       Impact factor: 4.056

Review 4.  Potential usefulness of digital imaging in clinical diagnostic radiology: computer-aided diagnosis.

Authors:  K Doi; M L Giger; R M Nishikawa; K R Hoffmann; H MacMahon; R A Schmidt
Journal:  J Digit Imaging       Date:  1995-02       Impact factor: 4.056

Review 5.  Computer-assisted detection of infectious lung diseases: a review.

Authors:  Ulaş Bağcı; Mike Bray; Jesus Caban; Jianhua Yao; Daniel J Mollura
Journal:  Comput Med Imaging Graph       Date:  2011-07-01       Impact factor: 4.790

6.  Classification of normal and abnormal lungs with interstitial diseases by rule-based method and artificial neural networks.

Authors:  S Katsuragawa; K Doi; H MacMahon; L Monnier-Cholley; T Ishida; T Kobayashi
Journal:  J Digit Imaging       Date:  1997-08       Impact factor: 4.056

Review 7.  Computer-Aided Diagnosis of Coal Workers' Pneumoconiosis in Chest X-ray Radiographs Using Machine Learning: A Systematic Literature Review.

Authors:  Liton Devnath; Peter Summons; Suhuai Luo; Dadong Wang; Kamran Shaukat; Ibrahim A Hameed; Hanan Aljuaid
Journal:  Int J Environ Res Public Health       Date:  2022-05-25       Impact factor: 4.614

8.  Quantitative analysis of geometric-pattern features of interstitial infiltrates in digital chest radiographs: preliminary results.

Authors:  S Katsuragawa; K Doi; H MacMahon; L Monnier-Cholley; J Morishita; T Ishida
Journal:  J Digit Imaging       Date:  1996-08       Impact factor: 4.056

9.  Development of CAD based on ANN analysis of power spectra for pneumoconiosis in chest radiographs: effect of three new enhancement methods.

Authors:  Eiichiro Okumura; Ikuo Kawashita; Takayuki Ishida
Journal:  Radiol Phys Technol       Date:  2014-01-12

10.  Detection and Visualisation of Pneumoconiosis Using an Ensemble of Multi-Dimensional Deep Features Learned from Chest X-rays.

Authors:  Liton Devnath; Zongwen Fan; Suhuai Luo; Peter Summons; Dadong Wang
Journal:  Int J Environ Res Public Health       Date:  2022-09-06       Impact factor: 4.614

  10 in total

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