On the Relationship between Physical Metrics and Numerical Observer Studies for the Evaluation of Image Reconstruction Algorithms
Physical metrics, such as the root mean squared distance, are simple to calculate, but we do not have a great confidence in their medical relevance. We have a greater confidence in numerical observer studies, but they are more complicated to carry out. Suppose that a claim is made that a particular physical metric yields similar results to a particular numerical observer study. How can we evaluate the validity of such a claim? In this paper we introduce the notion of “rank ordering similarity” to measure the relationship (closeness, similarity) between algorithm performance measures. We use this notion to study the relationship between a particular physical metric (relative clipped error) and a particular numerical observer method (ROC on threshold). We also investigate the role of contrast in this context.
KeywordsRank Ordering Human Observer Physical Metrics Statistical Pattern Recognition Image Reconstruction Algorithm
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