Literature DB >> 8531883

Computer-aided diagnosis for interstitial infiltrates in chest radiographs: optical-density dependence of texture measures.

J Morishita1, K Doi, S Katsuragawa, L Monnier-Cholley, H MacMahon.   

Abstract

We have been developing a computerized scheme for automated detection and characterization of interstitial infiltrates based on the Fourier transform of lung texture. To improve the performance of the scheme, which was developed using digitized screen-film radiographs, optical-density dependence of both the gradient of the film used and the system noise associated with the laser scanner were investigated. Two hundred chest radiographs, including 100 abnormal cases with interstitial infiltrates, were digitized using a laser scanner. The root-mean-square (RMS) variations and the first moments of the power spectra, which correspond to the magnitude and coarseness of lung texture, were determined by Fourier transform of lung textures in numerous regions of interest (ROIs). The RMS variation was dependent upon the average optical density in the ROI, though no obvious trend existed for the first moment of the power spectrum. Dependence of the RMS variations on optical density was corrected for using the gradient curve of the film. Also, system noise associated with the laser scanner was corrected. Results indicated that the specificity was improved from 81% (without correction) to 89% (with corrections), without any loss of sensitivity (90%). Thus, the correspondence between the computer output and consensus interpretation of radiologists was improved with the new scheme compared to the previous one. This improved computerized scheme may be useful to radiologists in detecting interstitial infiltrates in chest radiographs.

Mesh:

Year:  1995        PMID: 8531883     DOI: 10.1118/1.597419

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  5 in total

1.  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

2.  Application of artificial neural networks for quantitative analysis of image data in chest radiographs for detection of interstitial lung disease.

Authors:  T Ishida; S Katsuragawa; K Ashizawa; H MacMahon; K Doi
Journal:  J Digit Imaging       Date:  1998-11       Impact factor: 4.056

Review 3.  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

4.  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

5.  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

  5 in total

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