dc.contributor |
Universitat de Barcelona |
dc.contributor.author |
Perez Moreno, Álvaro |
dc.contributor.author |
Dominguez, Mara |
dc.contributor.author |
Migliorelli, Federico |
dc.contributor.author |
Gratacós Solsona, Eduard |
dc.contributor.author |
Palacio i Riera, Montserrat |
dc.contributor.author |
Bonet Carné, Elisenda |
dc.date |
2018-10-24T16:54:32Z |
dc.date |
2019-03-30T06:10:25Z |
dc.date |
2018-09-30 |
dc.date |
2018-10-24T16:54:32Z |
dc.identifier.citation |
0278-4297 |
dc.identifier.citation |
682662 |
dc.identifier.uri |
http://hdl.handle.net/2445/125608 |
dc.format |
40 p. |
dc.format |
application/pdf |
dc.language.iso |
eng |
dc.publisher |
American Institute of Ultrasound in Medicine |
dc.relation |
Versió postprint del document publicat a: https://doi.org/10.1002/jum.14824 |
dc.relation |
Journal of Ultrasound in Medicine, 2018 |
dc.relation |
https://doi.org/10.1002/jum.14824 |
dc.rights |
(c) American Institute of Ultrasound in Medicine, 2018 |
dc.rights |
info:eu-repo/semantics/openAccess |
dc.subject |
Ecografia fetal |
dc.subject |
Fetal ultrasonic imaging |
dc.title |
Clinical feasibility of quantitative ultrasound texture analysis: A robustness study using fetal lung ultrasound images |
dc.type |
info:eu-repo/semantics/article |
dc.type |
info:eu-repo/semantics/acceptedVersion |
dc.description.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. |