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Proceedings Paper

Computerized analysis of interstitial lung diseases on chest radiographs based on lung texture, geometric-pattern features, and artificial neural networks
Author(s): Takayuki Ishida; Shigehiko Katsuragawa; Katsumi Nakamura; Kazuto Ashizawa; Heber MacMahon; Kunio Doi
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Paper Abstract

For computerized detection of interstitial lung disease on chest radiographs, we developed three different methods: texture analysis based on the Fourier transform, geometric- pattern feature analysis, and artificial neural network (ANN) analysis of image data. With these computer-aided diagnostic methods, quantitative measures can be obtained. To improve the diagnostic accuracy, we investigated combined classification schemes by using the results obtained with the three methods for distinction between normal and abnormal chest radiographs with interstitial opacities. The sensitivities of texture analysis, geometric analysis, and ANN analysis were 88.0+/- 1.6%, 91.0+/- 2.6%, and 87.5+/- 1.9%, respectively, at a specificity of 90.0%, whereas the sensitivity of a combined classification scheme with the logical OR operation was improved to 97.1%+/- 1.5% at the same specificity of 90.0%. The combined scheme can achieve higher accuracy than the individual methods for distinction between normal and abnormal cases with interstitial opacities.

Paper Details

Date Published: 9 May 2002
PDF: 8 pages
Proc. SPIE 4684, Medical Imaging 2002: Image Processing, (9 May 2002); doi: 10.1117/12.467096
Show Author Affiliations
Takayuki Ishida, Hiroshima International Univ. (Japan)
Shigehiko Katsuragawa, Nippon Bunri Univ. (Japan)
Katsumi Nakamura, Univ. of Occupational and Environmental Health (Japan)
Kazuto Ashizawa, Nagasaki Univ. (United States)
Heber MacMahon, Univ. of Chicago (United States)
Kunio Doi, Univ. of Chicago (United States)


Published in SPIE Proceedings Vol. 4684:
Medical Imaging 2002: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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