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11 May 1994 Narrow bandwidth spectral analysis of the textures of interstitial lung diseases
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The object of this study was to develop a classifier for distinguishing between regions-of-interest (ROIs) from normal lung radiographs and ROIs from radiographs showing interstitial lung disease. The method used was to estimate the covariance statistics of the ROIs of the lung interstitial space and, based on the estimate, to design filters for isolating statistically significant components of the spectrum. The energy of filtered images was used as a classifier. Additionally, the filtered images were analyzed and classified using a convolution neural network (CNN). The procedure used to generate the filters was: (1) Convert 2D neighborhoods of pixels to vectors. (2) Form the sample covariance matrix from the vectors. (3) Compute the eigenvectors and eigenvalues of the matrix. (4) Convert the eigenvectors back to 2D form and use as filters. The images selected for study included normal lungs, and lungs with different types and profusions of pneumoconiosis opacities. One group of ROIs of the interstitial space was used to design filters. Another group was used as a test of classification accuracy. The results showed that the designed classifier was effective in discriminating ROIs with small pneumoconiosis opacities from normal ROIs.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Brian Krasner, Shih-Chung Benedict Lo, and Seong Ki Mun "Narrow bandwidth spectral analysis of the textures of interstitial lung diseases", Proc. SPIE 2167, Medical Imaging 1994: Image Processing, (11 May 1994);

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