Abstract
The human spine undergoes degenerative changes with age. One of the symptoms of aging is osteoporosis, which is manifested by loss of bone mass and reduced density of trabeculae. Previous studies have shown that some image features are correlated with age. In this research, image features’ values were obtained using textural analysis. CCTA images of the thoracic vertebra of patients of different ages were analyzed using qMaZda software. The correlation between features’ values and the age of the person was determined using Spearman and Pearson coefficients. The research was carried out on two groups: a group consisting of male and female patients, and a group consisting of only women. In the entire research group, the image attributes derived from GRLM and GLCM showed the highest correlation with age. In the female group, GLCM features were the most correlated with age; moreover, the values of the correlation’s strength were noticeably higher than the highest values obtained in the group of both sexes. The influence of sex and image parameters on the relationship between age and textural features is discussed.
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This work was financed by the AGH – University of Science and Technology, Faculty of EAIIB, KBIB no 16.16.120.773.
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Węgrzyn, W., Pierzchała, M., Bałon, P., Banyś, R.P., Piórkowski, A. (2022). Assessment of Correlations Between Age and Textural Features of CT Images of Thoracic Vertebrae. In: Choraś, M., Choraś, R.S., Kurzyński, M., Trajdos, P., Pejaś, J., Hyla, T. (eds) Progress in Image Processing, Pattern Recognition and Communication Systems. CORES IP&C ACS 2021 2021 2021. Lecture Notes in Networks and Systems, vol 255. Springer, Cham. https://doi.org/10.1007/978-3-030-81523-3_10
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DOI: https://doi.org/10.1007/978-3-030-81523-3_10
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