Classification of Hepatic Lesions using the Matching Metric
Aaron Adcock, Daniel L. Rubin, Gunnar Carlsson · arXiv (Cornell University) · 2012
In this paper we present a methodology of classifying hepatic (liver) lesions using multidimensional persistent homology, the matching metric (also called the bottleneck distance), and a support vector machine. We present our classification results on a dataset of 132 lesions that have been outlined and annotated by radiologists. We find that topological features are useful in the classification of hepatic lesions. We also find that two-dimensional persistent homology outperforms one-dimensional persistent homology in this application.