Ultrasonic backscatter signal processing technique for the characterization of animal lymph node

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Abstract

Quantitative ultrasonic characterization of biological soft tissues has become in recent years an essential tool in the non-invasive-non-destructive assessment of physical properties of the microstructure of tissues, due to the potential for estimating acoustic parameters associated to density characteristics, distribution and heterogeneity of histological samples, as well as making the construction of improved quantitative images that support processes of clinical diagnosis. This paper presents the implementation of computational methods based on spectral analysis techniques for the construction of parametric ultrasonic images of animal suprascapular lymph node, which is an important tissue for the analysis of animal health risk or animal health. The computational algorithms were implemented based on the estimation of the acoustic attenuation coefficient dependent of the frequency and integrated backscatter coefficient (IBC). These computational procedures automatically processed 400 ultrasonic echoes acquired in a region of interest of 4 cm2 for each sample of lymph node, which it was exposed to an incident ultrasonic field of 2.25 MHz with bandwidth of 1 MHz @ −3 dB. The results allowed parametric identification of nodule structures as germinal nodules, which are hardly identified in conventional qualitative ultrasound images. Finally ultrasonic parametric characterization of biological study samples provides potential quantitative indicators, which are so much accurate in the estimation of histonormality.

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Villamarín, J. A., Montilla, D. A., Potosi, O. M., Londoño, L. F., Muñoz, F. G., & Gutierrez, E. W. (2018). Ultrasonic backscatter signal processing technique for the characterization of animal lymph node. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10657 LNCS, pp. 702–709). Springer Verlag. https://doi.org/10.1007/978-3-319-75193-1_84

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