  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.
