Aims and objectives
Texture is defined as the quantification of the spatial distribution of repeating patterns.
It is used to translate the homogeneous appearance or not of an object on an image andis the subject of numerous studies for many years with the ambition to set the properties of a material by analyzingits image. The use of this toolin medical imaging is relatively recent,
with a renewed interest due to technical improvements that are expected from modern imaging an almost virtual reality. Texture...
Methods and materials
Acommercial CIRS062M phantom was used with commercial homogeneous inserts and home-made inserts made of cork,
wood and heterogeneous mixture of Ecoflex® (EF)+ random arrangementsof pure carbon fragments gelled inagaroseor polystyrene beads.
5 home-made inserts were placed in the phantom (fig.
1 and 2). CT acquisitions were made on a device 64 multi detector arrays Discovery CT750 HD (GE Healthcare,
WI). All settings that can be changed manually were considered: tube voltage from 80...
Out ouf 34 Lifex texture indices,
12 indices had a random variation when applying the different acquisition parameters: all histogramm indices,
SZLGE and LZLGE (example fig.
5). Out of the 22 remaining indices,7 indices make it impossible to differentiate the textures from each other: homogeneity,
Some of them allowedto differentiate the homogeneous texture from the others but not the heterogeneous...
we selected 9 texture indices which seem more robust because they presentno ora monotone variation when applying different CT acquistion parameters they make it possible to differentiate the textures between them. The entropy of the co-occurrence matrix is one of these indices and it is interesting to note that this index is often relevant in the in vivo studies conducted so far.
This study suggests that the standardization ofCT acquisitions is mandatory prior tothe...
Texture analysis of medical images.
Clin Radiol 2004; 59:1061–1069. 2-Miles Kenneth.
CT texture analysis using the filtration-histogram method: what do the measurements mean? Cancer Imaging.
2013 Sep 23;13(3):400-6. 3-Fave Xenia.
Preliminary investigation into sources of uncertainty in quantitative imaging features Computerized Medical Imaging and Graphics 2015 54–61. 4-Huang Yan-Qi.
Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph...