Perez‐Moreno, A; Dominguez, M; Migliorelli, F; Gratacos, E; Palacio, M; Bonet-Carne, E; (2018) Clinical Feasibility of Quantitative Ultrasound Texture Analysis: A Robustness Study Using Fetal Lung Ultrasound Images. Journal of Ultrasound in Medicine 10.1002/jum.14824. (In press).

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Abstract

OBJECTIVES: To compare the robustness of several methods based on quantitative ultrasound (US) texture analysis to evaluate its feasibility for extracting features from US images to use as a clinical diagnostic tool. METHODS: We compared, ranked, and validated the robustness of 5 texture‐based methods for extracting textural features from US images acquired under different conditions. For comparison and ranking purposes, we used 13,171 non‐US images from widely known available databases (OUTEX [University of Oulu, Oulu, Finland] and PHOTEX [Texture Lab, Heriot‐Watt University, Edinburgh, Scotland]), which were specifically acquired under different controlled parameters (illumination, resolution, and rotation) from 103 textures. The robustness of those methods with better results from the non‐US images was validated by using 666 fetal lung US images acquired from singleton pregnancies. In this study, 2 similarity measurements (correlation and Chebyshev distances) were used to evaluate the repeatability of the features extracted from the same tissue images. RESULTS: Three of the 5 methods (gray‐level co‐occurrence matrix, local binary patterns, and rotation‐invariant local phase quantization) had favorably robust performance when using the non‐US database. In fact, these methods showed similarity values close to 0 for the acquisition variations and delineations. Results from the US database confirmed robustness for all of the evaluated methods (gray‐level co‐occurrence matrix, local binary patterns, and rotation‐invariant local phase quantization) when comparing the same texture obtained from different regions of the image (proximal/distal lungs and US machine brand stratification). CONCLUSIONS: Our results confirmed that texture analysis can be robust (high similarity for different condition acquisitions) with potential to be included as a clinical tool.

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