A preparatory study to choose similarity metrics for left-ventricle segmentations comparison

Abstract

In medical image processing and analysis it is often required to perform segmentation for quantitative measures of extent, volume and shape. The validation of new segmentation methods and tools usually implies comparing their various outputs among themselves (or with a ground truth), using similarity metrics. Several such metrics are proposed in the literature but it is important to select those which are relevant for a particular task as opposed to using all metrics and therefore avoiding additional computational cost and redundancy. A methodology is proposed which enables the assessment of how different similarity and discrepancy metrics behave for a particular comparison and the selection of those which provide relevant data.