Szczegóły publikacji
Opis bibliograficzny
Assessment of correlations between age and textural features of CT images of thoracic vertebrae / Weronika Węgrzyn, Monika PIERZCHAŁA, Paulina Bałon, Robert Paweł Banyś, Adam PIÓRKOWSKI // W: Progress in image processing, pattern recognition and communication systems : proceedings of the conference (CORES, IP&C, ACS) – June 28–30 2021 / eds. Michal Choraś, [et al.]. — Cham : Springer Nature Switzerland, cop. 2022. — (Lecture Notes in Networks and Systems ; ISSN 2367-3370 ; vol. 255). — ISBN: 978-3-030-81522-6; e-ISBN: 978-3-030-81523-3. — S. 107–115. — Bibliogr., Abstr. — Publikacja dostępna online od: 2021-08-18
Autorzy (5)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 136489 |
|---|---|
| Data dodania do BaDAP | 2021-09-29 |
| DOI | 10.1007/978-3-030-81523-3_10 |
| Rok publikacji | 2022 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Czasopismo/seria | Lecture Notes in Networks and Systems |
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.
