TEXTURAL STATISTICAL FEATURES OF ULTRASOUND IMAGING OF THYROID NODULES IN THE ASSESSMENT OF MALIGNANCY STATUS
https://doi.org/10.56304/S2079562924050300
EDN: VHNELK
Abstract
The paper presents the results of a study of the possibility of using textural statistical features to classify images of ultrasound diagnostics of thyroid nodules. Ultrasound diagnostics has a significant potential for quantitative diagnostics. New information technologies allow us to identify characteristics that complement the classical methods of image analysis in medicine.
About the Authors
A. V. Manaev
National Medical Research Center of Endocrinology of the Ministry of Health of the Russian Federation;
National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)
Russian Federation
Russian Federation
A. A. Trukhin
National Medical Research Center of Endocrinology of the Ministry of Health of the Russian Federation;
National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)
Russian Federation
Russian Federation
S. M. Zakharova
National Medical Research Center of Endocrinology of the Ministry of Health of the Russian Federation;
National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)
Russian Federation
Russian Federation
E. A. Troshina
National Medical Research Center of Endocrinology of the Ministry of Health of the Russian Federation;
National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)
Russian Federation
Russian Federation
N. G. Mokrysheva
National Medical Research Center of Endocrinology of the Ministry of Health of the Russian Federation
Russian Federation
Russian Federation
A. A. Garmash
National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)
Russian Federation
Russian Federation
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Review
For citations:
Manaev A.V., Trukhin A.A., Zakharova S.M., Troshina E.A., Mokrysheva N.G., Garmash A.A. TEXTURAL STATISTICAL FEATURES OF ULTRASOUND IMAGING OF THYROID NODULES IN THE ASSESSMENT OF MALIGNANCY STATUS. Nuclear Physics and Engineering. 2025;16(2):237-244. (In Russ.) https://doi.org/10.56304/S2079562924050300. EDN: VHNELK
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