CatSIM: A Categorical Image Similarity Metric
This work addresses the need for improved similarity metrics in image analysis, particularly for categorical data, but appears incremental as it builds on existing structural similarity paradigms.
The authors tackled the problem of measuring similarity in categorical images by introducing CatSIM, a metric that is robust to small location perturbations and can compare arbitrary regions, and they evaluated it on artificial datasets, image quality assessments, and two imaging applications.
We introduce CatSIM, a new similarity metric for binary and multinary two- and three-dimensional images and volumes. CatSIM uses a structural similarity image quality paradigm and is robust to small perturbations in location so that structures in similar, but not entirely overlapping, regions of two images are rated higher than using simple matching. The metric can also compare arbitrary regions inside images. CatSIM is evaluated on artificial data sets, image quality assessment surveys and two imaging applications