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Proceedings Paper

Variations in algorithm implementation among quantitative texture analysis software packages
Author(s): Joseph J. Foy; Prerana Mitta; Lauren R. Nowosatka; Kayla R. Mendel; Hui Li; Maryellen L. Giger; Hania Al-Hallaq; Samuel G. Armato III
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Paper Abstract

Open-source texture analysis software allows for the advancement of radiomics research. Variations in texture features, however, result from discrepancies in algorithm implementation. Anatomically matched regions of interest (ROIs) that captured normal breast parenchyma were placed in the magnetic resonance images (MRI) of 20 patients at two time points. Six first-order features and six gray-level co-occurrence matrix (GLCM) features were calculated for each ROI using four texture analysis packages. Features were extracted using package-specific default GLCM parameters and using GLCM parameters modified to yield the greatest consistency among packages. Relative change in the value of each feature between time points was calculated for each ROI. Distributions of relative feature value differences were compared across packages. Absolute agreement among feature values was quantified by the intra-class correlation coefficient. Among first-order features, significant differences were found for max, range, and mean, and only kurtosis showed poor agreement. All six second-order features showed significant differences using package-specific default GLCM parameters, and five second-order features showed poor agreement; with modified GLCM parameters, no significant differences among second-order features were found, and all second-order features showed poor agreement. While relative texture change discrepancies existed across packages, these differences were not significant when consistent parameters were used.

Paper Details

Date Published: 27 February 2018
PDF: 7 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105751K (27 February 2018); doi: 10.1117/12.2292573
Show Author Affiliations
Joseph J. Foy, The Univ. of Chicago (United States)
Prerana Mitta, The Univ. of Chicago (United States)
Lauren R. Nowosatka, The Univ. of Chicago (United States)
Kayla R. Mendel, The Univ. of Chicago (United States)
Hui Li, The Univ. of Chicago (United States)
Maryellen L. Giger, The Univ. of Chicago (United States)
Hania Al-Hallaq, The Univ. of Chicago (United States)
Samuel G. Armato III, The Univ. of Chicago (United States)

Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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