A Sisojevs, A Tatarinov, A Chaplinska. Evaluation of Factors-of-Interest in Bone Mimicking Models Based on DFT Analysis of Ultrasonic Signals. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2023), 914-919 pp. 2023.
Bibtex citation:
Bibtex citation:
@inproceedings{15854_2023,
author = {A Sisojevs and A Tatarinov and A Chaplinska},
title = {Evaluation of Factors-of-Interest in Bone Mimicking Models Based on DFT Analysis of Ultrasonic Signals},
journal = {In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2023)},
pages = {914-919},
year = {2023}
}
author = {A Sisojevs and A Tatarinov and A Chaplinska},
title = {Evaluation of Factors-of-Interest in Bone Mimicking Models Based on DFT Analysis of Ultrasonic Signals},
journal = {In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2023)},
pages = {914-919},
year = {2023}
}
Abstract: Bone fragility in osteoporosis is associated with a decrease in the thickness of the cortical layer CTh in long bones and the development of internal porosity P. In the present work, an attempt was made to predict the factors-of-interest CTh and P based on the pattern recognition approach, where DFT analysis was applied to ultrasonic signals in surface transmission through a soft tissue layer. Compact bone was modeled with PMMA plates with gradual changes in CTh from 2 to 6 mm, and internal porosity P was created by drilling where the thickness of the porous layer P varied from 0 to 100% of CTh. The estimation method was based on a statistical analysis of the magnitude of the DFT spectrum of the ultrasonic signals. Decision rules were mathematical criteria calculated as ratios between the envelope functions of the magnitudes. Each of the objects was chosen in turn as a test object, while other specimens composed the training set. The results of the experiments showed the potential effectiveness of the CTh and P prediction, while additional physical parameters may be used as decision rules to improve the reliability of the diagnosis.