Improving the quality of tomographic images by the methods of the computer-aided measuring systems

A. I. Chulichkov; E. M. Molkov; S. I. Prutjan

doi:10.1117/12.2586935

4 January 2021 Improving the quality of tomographic images by the methods of the computer-aided measuring systems

A. I. Chulichkov, E. M. Molkov, S. I. Prutjan

Author Affiliations +

Proceedings Volume 11605, Thirteenth International Conference on Machine Vision; 1160513 (2021) https://doi.org/10.1117/12.2586935
Event: Thirteenth International Conference on Machine Vision, 2020, Rome, Italy

Abstract

The development of methods for improving the quality of tomographic images is an urgent task, as the presence of artifacts and insufficient sharpness of tomography result can cause erroneous decisions in medical diagnostics, when analyzing the structure of geological cores, etc. It is assumed that several artifacts arise due to inadequate a priori information involved in constructing the result of tomography, and a two-stage method is proposed for constructing an estimate of the distribution of the absorption coefficient of the sample. At the first stage, without using a priori information about the internal structure, the methods of the theory of computer-aided measuring systems construct an estimate of the absorption coefficient, which results in a blurred image of the internal structure of the object. At the second stage, the resolution of this image is increased by the method of moving averages with coefficients calculated from the condition of maximum accuracy for estimating the brightness of the central pixel of the window. Further, this estimate is refined from the natural conditions of the non-negativeness of the estimated brightness. Examples of the application of the method for assessing the structure of a child's tooth are given.

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A. I. Chulichkov, E. M. Molkov, and S. I. Prutjan "Improving the quality of tomographic images by the methods of the computer-aided measuring systems", Proc. SPIE 11605, Thirteenth International Conference on Machine Vision, 1160513 (4 January 2021); https://doi.org/10.1117/12.2586935

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