| Autor: |
Arttu, Peuna, Joonas, Hekkala, Marianne, Haapea, Jana, Podlipská, Ali, Guermazi, Simo, Saarakkala, Miika T, Nieminen, Eveliina, Lammentausta |
| Rok vydání: |
2017 |
| Předmět: |
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| Zdroj: |
Journal of magnetic resonance imaging : JMRI. 47(5) |
| ISSN: |
1522-2586 |
| Popis: |
Texture analysis methods based on gray level co-occurrence matrices (GLCM) can be optimized to probe the spatial correspondence information from knee MRI TTo introduce a novel implementation of GLCM algorithm optimized for cartilage texture analysis; to evaluate the performance of the designed algorithm against mean TCase control.Eighty symptomatic osteoarthritis patients and 64 asymptomatic controls.Multislice multiecho spin echo sequence on a 3T MRI system.The TSymptomatic and asymptomatic subjects were compared using Mann-Whitney U-test. Repeatability of different features was addressed using the concordance correlation coefficient (CCC). Spearman's correlations between the features were determined.The algorithm displayed excellent performance in discerning symptomatic and asymptomatic subjects. Fifteen features provided a significant difference between the groups (P ≤ 0.05) and 12 of those had P values smaller than the mean TWith careful parameter and feature selection and algorithm optimization, texture analysis provides a powerful tool for assessing knee osteoarthritis with more sensitive detection of cartilage degeneration compared to the mean value of the T2 Technical Efficacy Stage 2 J. Magn. Reson. Imaging 2018;47:1316-1327. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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