Generalized Overlap Measures for Evaluation and Validation in Medical Image Analysis
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Abstract
Measures of overlap of labelled regions of images, such as the Dice and Tanimoto coefficients, have been extensively used to evaluate image registration and segmentation algorithms. Modern studies can include multiple labels defined on multiple images yet most evaluation schemes report one overlap per labelled region, simply averaged over multiple images. In this paper, common overlap measures are generalized to measure the total overlap of ensembles of labels defined on multiple test images and account for fractional labels using fuzzy set theory. This framework allows a single "figure-of-merit" to be reported which summarises the results of a complex experiment by image pair, by label or overall. A…
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Keywords
- Artificial intelligence
- Segmentation
- Ground truth
- Hausdorff distance
- Computer science
- Pattern recognition (psychology)
- Image segmentation
- Image registration
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