To image analysis in computed tomography

17 March 2017 To image analysis in computed tomography

Marina Chukalina, Dmitry Nikolaev, Anastasia Ingacheva, Alexey Buzmakov, Ivan Yakimchuk, Victor Asadchikov

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Proceedings Volume 10341, Ninth International Conference on Machine Vision (ICMV 2016); 103411B (2017) https://doi.org/10.1117/12.2268616
Event: Ninth International Conference on Machine Vision, 2016, Nice, France

Abstract

The presence of errors in tomographic image may lead to misdiagnosis when computed tomography (CT) is used in medicine, or the wrong decision about parameters of technological processes when CT is used in the industrial applications. Two main reasons produce these errors. First, the errors occur on the step corresponding to the measurement, e.g. incorrect calibration and estimation of geometric parameters of the set-up. The second reason is the nature of the tomography reconstruction step. At the stage a mathematical model to calculate the projection data is created. Applied optimization and regularization methods along with their numerical implementations of the method chosen have their own specific errors. Nowadays, a lot of research teams try to analyze these errors and construct the relations between error sources. In this paper, we do not analyze the nature of the final error, but present a new approach for the calculation of its distribution in the reconstructed volume. We hope that the visualization of the error distribution will allow experts to clarify the medical report impression or expert summary given by them after analyzing of CT results. To illustrate the efficiency of the proposed approach we present both the simulation and real data processing results.

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Marina Chukalina, Dmitry Nikolaev, Anastasia Ingacheva, Alexey Buzmakov, Ivan Yakimchuk, and Victor Asadchikov "To image analysis in computed tomography", Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 103411B (17 March 2017); https://doi.org/10.1117/12.2268616

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