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.
Keywords: image processing; quantitative ultrasound; robustness; texture analysis; ultrasound.
© 2018 by the American Institute of Ultrasound in Medicine.