K-nearest neighbor classification for the differentiation between freshly excised and decellularized rat kidneys using envelope statistics

Decellularization is a technique that permits the removal of cells from intact tissue while preserving the extracellular matrix structure. It has many applications in the fields of regenerative medicine and tissue engineering. Evaluating the efficiency of cell removal from the tissue non-invasively remains a challenge. This work aims to investigate the use of the k-nearest neighbor classifier using envelope statistics, for the differentiation between freshly excised and decellularized rat kidneys. Two probability distributions were fitted with the histogram of the ultrasound backscatter signal envelope intensities: the Rayleigh and Generalized Gamma. Three fit parameters were extractedfrom these distributions andfed into the classifier. The classification resulted in an AUC of 0.93 and an accuracy of 92%. Future work include incorporating other distributions to further improve the accuracy of this classifier, as well as investigating other classifiers of interest.