Classification of Hepatic Lesions using the Matching Metric

2012/10/02 by Adcock, Aaron, Rubin, Daniel, Carlsson, Gunnar · 2 citations
#Algebraic Topology (math.AT) #Computational Geometry (cs.CG) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Mathematics

paper · doi:10.48550/arxiv.1210.0866

Abstract

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.

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• Persistent homology-based segmentation tool for membrane images
• Topological Invariant-Based Iris Identification via Digital Homology and Machine Learning

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