2017
DOI: 10.18287/2412-6179-2017-41-5-726-731
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Pulmonary emphysema recognition by CT scan

Abstract: We discuss a simple method for automatic recognition of pulmonary emphysema in three-dimensional computer tomography (CT) images. This technique allows one to quantify the disease progress, calculating some numerical characteristics, such as the percentage of the lung tissue affected, as well as visualizing its location and intensity histogram in the region of interest. An experiment on the test data shows that the recognition error is not higher than 7.5%.

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Cited by 14 publications
(8 citation statements)
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“…This could be mitigated by increasing the number of projections added at each stage of the process. Figure 7 illustrates the averaged experimental profiles of two types: reconstruction with stopping at a fixed stage (that is, after a fixed number of projections is collected), and with stopping using the constructed rules (14)- (16). The presented results confirm that the monitored reconstruction approach in an "anydose" model allows to reduce the mean reconstruction error with a given mean number of analyzed projections.…”
Section: Discussionmentioning
confidence: 56%
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“…This could be mitigated by increasing the number of projections added at each stage of the process. Figure 7 illustrates the averaged experimental profiles of two types: reconstruction with stopping at a fixed stage (that is, after a fixed number of projections is collected), and with stopping using the constructed rules (14)- (16). The presented results confirm that the monitored reconstruction approach in an "anydose" model allows to reduce the mean reconstruction error with a given mean number of analyzed projections.…”
Section: Discussionmentioning
confidence: 56%
“…3) For implementation of the stopping rule (15), the 2 -norm of the last reconstruction result R T 2 . 4) For implementation of the stopping rule (16), the value of the Radon invariant S(R T ), which can be calculated by analyzing the obtained projections x 1 , x 2 , ... , x n . The method of modelling of the next result introduced in [37] is not applicable for the case of tomography (as the assumption of the next projection x n+1 having the same value as one of the previously acquired will lead to the same reconstruction result), thus in order to estimate the expected distance E n ( R n − R n+1 2 ) other methods should be used, such as methods of time series forecasting.…”
Section: F Implementing the Stopping Rulesmentioning
confidence: 99%
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“…The issue of tomographic images analysis automation for other internal organs also becomes more and more relevant. So in the article [13] the simplest technology of automatic recognition of emphysema of lungs by sets of two-dimensional diagnostic images of computed tomography is considered. In [14], a method for segmentation of organs of the retroperitoneal space on tomographic images based on the level function was proposed.…”
Section: Introductionmentioning
confidence: 99%