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

Medical Physics

Volumn 38, Issue 8, 2011, Pages 4512-4517

  Prieto, Gabriel ^a   Guibelalde, Eduardo ^a   Chevalier, Margarita ^a   Turrero, Agustn ^a  

^a UNIVERSIDAD COMPLUTENSE DE MADRID   (Spain)

Author keywords

CDMAM; image quality; mammography; model observer; MS SSIM

Indexed keywords

CLUSTERING ALGORITHMS; IMAGE QUALITY; MAMMOGRAPHY; STATISTICAL TESTS; VISIBILITY;

CDMAM; CONTRAST THRESHOLD; CONTRAST-DETAIL CURVE; CROSS CORRELATIONS; HUMAN PERFORMANCE; MODEL OBSERVER; MS-SSIM; STRUCTURAL SIMILARITY;

MEDICAL IMAGING;

EID: 79961078796     PISSN: 00942405     EISSN: None     Source Type: Journal    
DOI: 10.1118/1.3605634     Document Type: Article

References (19)

1. B. Girod, What's wrong with mean-squared error., in Digital Images and Human Vision, edited by, A. B. Watson, (MIT, Cambridge, MA, 1993), pp. 207-220.
2. A. E. Burgess, The Rose model, revisited., J. Opt. Soc. Am. A 16, 633-646 (1999). 10.1364/JOSAA.16.000633 (Pubitemid 129582210)
3. K. J. Myers, Ideal observer models of visual signal detection., in Handbook of Medical Imaging, Physics and Psycophysics, edited by, J. Beutel, H. Kundel, and, R. Van Metter, (SPIE, Bellingham, WA, 2000), Vol. 1, pp. 558-592.
4. H. H. Barrett, K. J. Myers, and R. F. Wagner, Beyond signal detection theory, application of optical instrumentation in medicine XIV and Picture Archiving and Communications (PACS IV) for medical applications, Newport Beach, CA.. Proceedings of the Society of Photo-optical Instrumentation Engineers, (Bellingham, WA, 1986), Vol. 626, pp. 231-239.
5. M. P. Eckstein, C. K. Abbey, and F. O. Bochud, A practical guide to model observers for visual detection in synthetic and natural noisy images., in Handbook of medical imaging, physics, and psychophysics, edited by, J. Beutel, H. Kundel, and, R. Van Metter, (SPIE, Bellingham, WA, 2000), Vol. 1, pp. 593-626.
6. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, Image Quality Assessment: From Error Visibility to Structural Similarity., IEEE Trans. Image Process. 13, pp. 600-612 (2004). 10.1109/TIP.2003.819861
7. Z. Wang and A. C. Bovik, Why is image quality assessment so difficult, IEEE Trans. Acoust., Speech, Signal Process. 4, 3313-3316 (2002) 10.1109/ICASSP.2002.5745362.
8. Z. Wang, E. Simoncelli, and A. Bovik, Multi-scale structural similarity for image quality assessment., in Proceedings of the 37th IEEE Asilomar Conference on Signals, Systems, and Computers, Pacific Groove, Ca, IEEE (2003), pp. 529-554.
9. D. M. Rouse and S. S. Hemami, Analyzing the Role of Visual Structure in the Recognition of Natural Image Content with Multi-Scale SSIM., Proc. SPIE 6806, 680615 (2009).
10. R. Dosselmann and X. D. Yang, A comprehensive assessment of the structural similarity index., Signal, Image, and Video Processing (Springer, London, 2009), Vol. 51, pp. 81-91.
11. K. R. Bijkerk, M. A. Thijssen, and T. H. Arnoldussen, Manual CDMAM-Phantom Type 3.4. (translation from the Dutch by S. van Woudenberg), University Medical Centre Nijmegen, July 2000.
12. R. M. Gagne, B. D. Gallas, and K. J. Myers, Toward objective and quantitative evaluation of imaging systems using images of phantoms., Med. Phys. 33, 83-95 (2006). 10.1118/1.2140117 (Pubitemid 43121036)
13. N. Karssemeijer and M. A. O. Thijssen, Determination of contrast-detail curves of mammography systems by automated image analysis., in Digital Mammography, edited by, K. Doi, R. Giger, R. M. Nishikawa, and, R. A. Scmidt, (Elsevier, Amsterdam, 1996), pp. 155-160.
14. R. Visser and N. Karssemeijer, CDCOM Manual: software for automated readout of CDMAM 3.4 images., CDCOM software, manual, and sample images are posted at www.euref.org, Last accessed June 2010.
15. W. S. Rasband, ImageJ, U. S. National Institutes of Health, Bethesda, MD, http://rsb.info.nih.gov/ij/plugins/index.html 1997-2007, Last accessed June 2011.
16. R. Rico, S. L. Muller, and G. Peter, Automatic scoring of CDMAN a dose study., Proc. SPIE, 5034, 164-173 (2003). 10.1117/12.480362
17. G Prieto, M. Chevalier, and E. Guibelalde, CDMAM image phantom software improvement for human observer assessment., in Lecture Notes in Computer Science 5116 Digital mammography, edited by, E. A. Krupinski, (Springer-Verlag, Berlin, Heidelberg 2008), Vol. 5116, pp. 181-187.
18. G. Prieto, M. Chevalier, and E. Guibelalde, A software tool to measure the geometric distortion in x-ray image systems., Proc. SPIE, 7622, p. 173 (2010).
19. K. C. Young, J. J. H. Cook, J. M. Oduko, and H. Bosmans, Comparison of software and human observers in reading images of the CDMAM test object to assess digital mammography systems., Proc. SPIE, 6142, 614206 (2006).