Assessment of Similarity Indices to Quantify Segmentation Accuracy of Scaffold Images for Tissue Engineering

摘要

Existing similarity metrics to compare the accuracy of n-Dimensional image segmentation with the corresponding ground truth is restricted to a limited set of volume fractions which, by themselves, lack robustness. This paper introduces a comprehensive list of linear and non-linear similarity measures widely used in such diverse fields as ecology, toxicology and patent trending. These metrics based on the binary "absence/presence" data were computed for assessing the delineation of tissue engineering scaffold images into porous and polymeric space using a wide variety of thresholding techniques.