Abstract
A Survey on Quantitative Metrics for Assessing the Quality of Fused Medical Images
Highlights
Image quality assessment plays an important role in many domains such as compression, fusion, registration and reconstruction
Users rate the images based on the effect of degradation and it varies from user to user whereas quantitative metrics, finds the difference in the images owing to process (Petrovic, 2007)
The fused image is analyzed with respect to different perspectives such as information, edge, contrast, shape, structure, correlation, noise and error based on the requirement of users or applications
Summary
Image quality assessment plays an important role in many domains such as compression, fusion, registration and reconstruction. The suitable fusion process for the specific combination of dataset is identified from the benchmark datasets using subjective and objective measures (Brain Images Source, 2014b; Brain Images Source, 2014a) With this brief introduction, we move on to the metrics related to analyzing the fused medical image and a short introduction on remote sensing images with its metrics, since the discussion of fusion techniques is not related to this study. The objective of this study is to group or classify the quantitative metrics of fused images under different categories such as information, noise, error, correlation and structural similarity measures for discussion and analysis. Further the importance of metrics in statistical analysis is illustrated using fused brain images
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