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Use of the cross-correlation component of the multiscale structural similarity metric (R metric) for the evaluation of medical images

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Prieto, G. and Guibelalde, E. and Chevalier, M. and Turrero, Agustín (2011) Use of the cross-correlation component of the multiscale structural similarity metric (R metric) for the evaluation of medical images. Medical physics, 38 (8). pp. 4512-4517. ISSN 0094-2405

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Official URL: http://scitation.aip.org/content/aapm/journal/medphys/38/8/10.1118/1.3605634


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Abstract

Purpose: The aim of the present work is to analyze the potential of the cross-correlation component of the multiscale structural similarity metric (R*) to predict human performance in detail detection tasks closely related with diagnostic x-ray images. To check the effectiveness of R, the authors have initially applied this metric to a contrast detail detection task. Methods: Threshold contrast visibility using the R* metric was determined for two sets of images of a contrast-detail phantom (CDMAM). Results from R and human observers were compared as far as the contrast threshold was concerned. A comparison between the R* metric and two algorithms currently used to evaluate CDMAM images was also performed. Results: Similar trends for the CDMAM detection task of human observers and R* were found in this study. Threshold contrast visibility values using R* are statistically indistinguishable from those obtained by human observers (F-test statistics: p > 0.05). Conclusions: These results using R* show that it could be used to mimic human observers for certain tasks, such as the determination of contrast detail curves in the presence of uniform random noise backgrounds. The R* metric could also outperform other metrics and algorithms currently used to evaluate CDMAM images and can automate this evaluation task.


Item Type:Article
Uncontrolled Keywords:CDMAM; image quality; mammography; model observer; MS-SSIM. EMTREE medical terms: algorithm; article; comparative study; computer assisted diagnosis; evaluation; female; human; image quality; mammography; methodology; observer variation; regression analysis; statistics. MeSH: Algorithms; Female; Humans; Mammography; Observer Variation; Phantoms, Imaging; Radiographic Image Interpretation, Computer-Assisted; Regression Analysis
Subjects:Sciences > Mathematics > Applied statistics
Medical sciences > Biology
ID Code:30855
Deposited On:12 Jun 2015 10:34
Last Modified:12 Jun 2015 10:34

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