V.E. Anciperov1, V.A. Kershner2, G.K. Mansurov3

1–3 Kotelnikov Institute of Radioengineering and Electronics (IRE) of RAS (Moscow, Russia)
1 antciperov@cplire.ru

Modern medicine actively uses the latest visualization tools, among which the most popular are computed tomography, magnetic resonance imaging, positron emission tomography, single-photon emission computed tomography, etc. A common feature of most of them is a relatively low level of electromagnetic radiation used, which leads to the fact that the corresponding detectors form images either in the FSD mode – detectors that count photons, or in a close mode of EID – detectors that integrate the energy of photons. Due to the specificity of these modes, images formed by the corresponding medical FSD/EID visualization devices are poorly suited for traditional methods of their recognition and analysis (diagnostics). Therefore, intensive research is constantly being conducted in this area to develop new methods and approaches to processing, storing and transmitting medical images.

The aim of the study is to develop methods for processing and presenting images that consider their discrete photo-counting nature. At the same time, the developed representations are focused on the possibility for a specialist to form reasonable conclusions (diagnostics) about the degree of similarity of the shape of objects in images with the shapes of previously observed objects (precedents).

A new method for presenting CT images based on the sampling representation obtained by the authors has been developed. The method is based on direct modeling of the mechanisms of primary processing of video data on the periphery of the human visual system.

The proposed in the work sampling representation (list-class) is very convenient in theoretical studies, but for practical applications it is more convenient to use combined representations of mixed list- and binned- classes. Experience in numerical testing and optimization of algorithms for the formation of the developed combined representation showed that it is possible to avoid computational problems associated with the processing of large amounts of data and adapt the approach to modern computer tomography problems.

Anciperov V.E., Kershner V.A., Mansurov G.K. Representation of CT images using receptive fields occupation numbers with fixed-size sample counts. Biomedicine Radioengineering. 2025. V. 28. № 5. P. 149–153. DOI: https:// doi.org/10.18127/j15604136-202505-30 (In Russian)

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