Radiomics in CT and MR imaging of the liver and pancreas: tools with potential for clinical application
Author:
Publisher
Springer Science and Business Media LLC
Subject
Urology,Gastroenterology,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology
Link
https://link.springer.com/content/pdf/10.1007/s00261-023-04071-0.pdf
Reference97 articles.
1. Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology 2016;278:563–77. https://doi.org/10.1148/radiol.2015151169.
2. Mayerhoefer ME, Materka A, Langs G, Häggström I, Szczypiński P, Gibbs P, et al. Introduction to Radiomics. Journal of Nuclear Medicine 2020;61:488–95. https://doi.org/10.2967/jnumed.118.222893.
3. Haralick RM, Shanmugam K, Dinstein I. Textural Features for Image Classification. IEEE Trans Syst Man Cybern 1973;SMC-3:610–21. https://doi.org/10.1109/TSMC.1973.4309314.
4. Xiaoou Tang. Texture information in run-length matrices. IEEE Transactions on Image Processing 1998;7:1602–9. https://doi.org/10.1109/83.725367.
5. Thibault G, Angulo J, Meyer F. Advanced Statistical Matrices for Texture Characterization: Application to Cell Classification. IEEE Trans Biomed Eng 2014;61:630–7. https://doi.org/10.1109/TBME.2013.2284600.
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