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3 July 2001 Automatic temporal subtraction of chest radiographs and its enhancement for lung cancers
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Abstract
In this study, we demonstrate an approach of using automatic edge detection and registration techniques for the temporal subtraction of chest radiographs. We also show that the contrasts of lung cancers were greatly enhanced. Difference images were obtained by subtracting an earlier chest radiograph from the later chest radiograph of the same patient. Prior to the subtraction, the lung areas were extracted by a convolution neural network. Segmented lungs and rib cages on both images were used to perform registration using directional edge extractors. Control points were selected prior to a warping process for the final registration. We also investigated the contrast enhancement of the lung cancer by analyzing the local area on the images. The signal-to-noise ratios at each cancer location were compared to evaluate the degree of improvement between the later chest image and the subtraction image. Our results indicated that the average signal-to-noise ratios at cancer locations were increased from 50% to 80%. The selected cases were collected for this study based on their subtleness. For each of this case, at least 4 radiologists out of 15 radiologists missed the cancer with a mean at 7.2 radiologists resulting from a recent observer study.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hui Zhao, Shih-Chung Benedict Lo, Matthew T. Freedman M.D., and Seong Ki Mun "Automatic temporal subtraction of chest radiographs and its enhancement for lung cancers", Proc. SPIE 4322, Medical Imaging 2001: Image Processing, (3 July 2001); https://doi.org/10.1117/12.431078
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