Abstract
There are several methods to evaluate objectively the quality of a digital image. For digital mammography, objective quality assessment must be performed without references. In a previous study, the authors investigated the use of a normalized anisotropic quality index (NAQI) to assess mammography images blindly in terms of noise and spatial resolution. Since the NAQI is used as a quality metric, it must not be highly dependent on the breast anatomy. Thus, in this work, we analyze the NAQI behavior with different breast anatomies. A computerized system was used to synthesize 2,880 anthropomorphic breast phantom images with a realistic range of anatomical variations. The results show that NAQI is only marginally dependent on breast anatomy when images are acquired without degradation (<12 %). However, for realizations that simulate the acquisition process in digital mammography, the NAQI is more sensitive (33 %) to variations arising from quantum noise. Thus, NAQI can be used in clinical practice to assess mammographic image quality.
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Acknowledgments
The authors would like to thank São Paulo Research Foundation (FAPESP grant #2013/18915-5) and the Brazilian Foundation for the Coordination of Improvement of Higher Education Personnel (CAPES grant #99999.014175/2013-04 and grant #88881.030443/2013-01) for the financial support given to this project. The authors would also like to acknowledge the support of the National Institutes of Health/National Cancer Institute grant 1R01-CA154444 and the U.S. National Institute of General Medical Sciences (P20 GM103446) from the National Institutes of Health. The content of this paper is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies. We thank Real Time Tomography (RTT) for providing assistance with image processing. We also acknowledge Abdullah-Al-Zubaer Imran, Niara Medley, Rick Emory, and Vernita Adkins for their assistance generating software breast phantoms and projections. ADAM is a member of the scientific advisory board and shareholder of RTT.
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Barufaldi, B. et al. (2016). The Effect of Breast Composition on a No-reference Anisotropic Quality Index for Digital Mammography. In: Tingberg, A., Lång, K., Timberg, P. (eds) Breast Imaging. IWDM 2016. Lecture Notes in Computer Science(), vol 9699. Springer, Cham. https://doi.org/10.1007/978-3-319-41546-8_30
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DOI: https://doi.org/10.1007/978-3-319-41546-8_30
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