Classification of hepatic lesions using the matching metric

Authors:
Aaron Adcock;Daniel Rubin;Gunnar Carlsson
Affiliations:
-;-;-
Venue:
Computer Vision and Image Understanding
Year:
2014

Citing 9
Cited 0

Computing Persistent Homology

Discrete & Computational Geometry
On the Local Behavior of Spaces of Natural Images

International Journal of Computer Vision
Multidimensional Size Functions for Shape Comparison

Journal of Mathematical Imaging and Vision
The Theory of Multidimensional Persistence

Discrete & Computational Geometry - 23rd Annual Symposium on Computational Geometry
A barcode shape descriptor for curve point cloud data

Computers and Graphics
On the Nonlinear Statistics of Range Image Patches

SIAM Journal on Imaging Sciences
Size functions for the morphological analysis of melanocytic lesions

Journal of Biomedical Imaging - Special issue on mathematical methods for images and surfaces
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)
On the Feasibility of Predicting Radiological Observations from Computational Imaging Features of Liver Lesions in CT Scans

HISB '11 Proceedings of the 2011 IEEE First International Conference on Healthcare Informatics, Imaging and Systems Biology

Quantified Score

Hi-index	0.00

Visualization

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.