Spectral analysis of full field digital mammography data.
The spectral content of mammograms acquired from using a full field digital mammography (FFDM) system are analyzed. Fourier methods are used to show that the FFDM image power spectra obey an inverse power law; in an average sense, the images may be considered as 1/f fields. Two data representations are analyzed and compared (1) the raw data, and (2) the logarithm of the raw data. Two methods are employed to analyze the power spectra (1) a technique based on integrating the Fourier plane with octave ring sectioning developed previously, and (2) an approach based on integrating the Fourier plane using rings of constant width developed for this work. Both methods allow theoretical modeling. Numerical analysis indicates that the effects due to the transformation influence the power spectra measurements in a statistically significant manner in the high frequency range. However, this effect has little influence on the inverse power law estimation for a given image regardless of the data representation or the theoretical analysis approach. The analysis is presented from two points of view (1) each image is treated independently with the results presented as distributions, and (2) for a given representation, the entire image collection is treated as an ensemble with the results presented as expected values. In general, the constant ring width analysis forms the foundation for a spectral comparison method for finding spectral differences, from an image distribution sense, after applying a nonlinear transformation to the data. The work also shows that power law estimation may be influenced due to the presence of noise in the higher frequency range, which is consistent with the known attributes of the detector efficiency. The spectral modeling and inverse power law determinations obtained here are in agreement with that obtained from the analysis of digitized film-screen images presented previously. The form of the power spectrum for a given image is approximately l/f2beta with beta approximately 1.4-1.5.
Heine
John J
JJ
Department of Radiology, College of Medicine, The University of South Florida, Tampa, USA. heinejj@moffitt.usf.edu
Velthuizen
Robert P
RP
D055592:Biophysical Phenomena; D001703:Biophysics; D001943:Breast Neoplasms / Q000175:diagnosis / Q000000981:diagnostic imaging; D005260:Female; D005583:Fourier Analysis; D006801:Humans; D016014:Linear Models; D008327:Mammography / Q000379:methods* / Q000706:statistics & numerical data; D011856:Radiographic Image Enhancement; D011857:Radiographic Image Interpretation, Computer-Assisted