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Med Phys. 2018 Apr;45(4):1529-1536. doi: 10.1002/mp.12809. Epub 2018 Mar 13.

Can we trust the calculation of texture indices of CT images? A phantom study.

Author information

1
Department of Imaging, Gustave Roussy Cancer Campus, Villejuif Cedex, France.
2
IR4M-UMR8081, CNRS, Univ Paris Sud, University Paris Saclay, Orsay Cedex, France.
3
Department of Biostatistics, Gustave Roussy Cancer, Villejuif, France.
4
Imagerie Moléculaire In Vivo, INSERM CEA CNRS University Paris Sud University Paris Saclay CEA, Orsay, France.
5
INSERM, U1030, Villejuif, France.
6
Research Department, Gustave Roussy Cancer Campus, Villejuif, France.

Abstract

PURPOSE:

Texture analysis is an emerging tool in the field of medical imaging analysis. However, many issues have been raised in terms of its use in assessing patient images and it is crucial to harmonize and standardize this new imaging measurement tool. This study was designed to evaluate the reliability of texture indices of CT images on a phantom including a reproducibility study, to assess the discriminatory capacity of indices potentially relevant in CT medical images and to determine their redundancy.

METHODS:

For the reproducibility and discriminatory analysis, eight identical CT acquisitions were performed on a phantom including one homogeneous insert and two close heterogeneous inserts. Texture indices were selected for their high reproducibility and capability of discriminating different textures. For the redundancy analysis, 39 acquisitions of the same phantom were performed using varying acquisition parameters and a correlation matrix was used to explore the 2 × 2 relationships. LIFEx software was used to explore 34 different parameters including first order and texture indices.

RESULTS:

Only eight indices of 34 exhibited high reproducibility and discriminated textures from each other. Skewness and kurtosis from histogram were independent from the six other indices but were intercorrelated, the other six indices correlated in diverse degrees (entropy, dissimilarity, and contrast of the co-occurrence matrix, contrast of the Neighborhood Gray Level difference matrix, SZE, ZLNU of the Gray-Level Size Zone Matrix).

CONCLUSIONS:

Care should be taken when using texture analysis as a tool to characterize CT images because changes in quantitation may be primarily due to internal variability rather than from real physio-pathological effects. Some textural indices appear to be sufficiently reliable and capable to discriminate close textures on CT images.

KEYWORDS:

computed tomography; phantom study; texture analysis

PMID:
29443389
DOI:
10.1002/mp.12809
[Indexed for MEDLINE]

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