Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters
335
Zitationen
5
Autoren
2010
Jahr
Abstract
Textural features such as entropy-first order, energy, maximal correlation coefficient, and low-gray level run emphasis exhibited small variations due to different acquisition modes and reconstruction parameters. Features with low level of variations are better candidates for reproducible tumor segmentation. Even though features such as contrast-NGTD, coarseness, homogeneity, and busyness have been previously used, our data indicated that these features presented large variations, therefore they could not be considered as a good candidates for tumor segmentation.
Ähnliche Arbeiten
TNM Classification of Malignant Tumours
1987 · 16.123 Zit.
A survey on deep learning in medical image analysis
2017 · 13.879 Zit.
Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening
2011 · 10.863 Zit.
The American Joint Committee on Cancer: the 7th Edition of the AJCC Cancer Staging Manual and the Future of TNM
2010 · 9.137 Zit.
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
2018 · 8.712 Zit.