"Scattering Signatures of Normal versus Abnormal Livers with Support Vector Machine Classification"
December 1, 2020
A paper co-authored by PhD students Jihye Baek and Sedigheh Poul and Professor Kevin Parker titled "Scattering signatures of normal versus abnormal livers with support vector machine classification" has been published in the December 2020 issue of Ultrasound in Medicine and Biology. Co-authors are Pfizer Inc. collaborators Terri Swanson and Dr. Teresa Tuthill. The abstract follows; more information can be found here.
Abstract: Fifty years of research on the nature of backscatter from tissues has resulted in a number of promising diagnostic parameters. We recently introduced two analyses tied directly to the biophysics of ultrasound scattering: the H-scan, based on a matched filter approach to distinguishing scattering transfer functions, and the Burr distribution for quantification of speckle patterns. Together, these analyses can produce at least five parameters that are directly linked to the mathematics of ultrasound in tissue. These have been measured in vivo in 35 rat livers under normal conditions and after exposure to compounds that induce inflammation, fibrosis, and steatosis in varying combinations. A classification technique, the support vector machine, is employed to determine clusters of the five parameters that are signatures of the different liver conditions. With the multiparametric measurement approach and determination of clusters, the different types of liver pathology can be discriminated with 94.6% accuracy.