22 October 2004 Eigen analysis for classifying chest x-ray images
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Abstract
A method first employed for face recognition has been employed to analyse a set of chest x-ray images. After marking certain common features on the images, they are registered by means of an affine transformation. The differences between each registered image and the mean of all images in the set are computed and the first K principal components are found, where K is less than or equal to the number of images in the set. These form eigenimages (we have coined the term 'eigenchests') from which an approximation to any one of the original images can be reconstructed. Since the method effectively treats each pixel as a dimension in a hyperspace, the matrices concerned are huge; we employ the method developed by Turk and Pentland for face recognition to make the computations tractable. The K coefficients for the eigenimages encode the variation between images and form the basis for discriminating normal from abnormal. Preliminary results have been obtained for a set of eigenimages formed from a set of normal chests and tested on separate sets of normals and patients with pneumonia. The distributions of coefficients have been observed to be different for the two test sets and work is continuing to determine the most sensitive method for detecting the differences.
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Philip J. Bones, Philip J. Bones, Anthony P. H. Butler, Anthony P. H. Butler, } "Eigen analysis for classifying chest x-ray images", Proc. SPIE 5562, Image Reconstruction from Incomplete Data III, (22 October 2004); doi: 10.1117/12.565175; https://doi.org/10.1117/12.565175
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