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Proceedings Paper

Mammographic structure: data preparation and spatial statistics analysis
Author(s): Arthur E. Burgess
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Paper Abstract

Detection of tumors in mammograms is limited by the very marked statistical variability of normal structure rather than image noise. This presentation reports investigation of the statistical properties of patient tissue structures in digitized x-ray projection mammograms, using a database of 105 normal pairs of craniocaudal images. The goal is to understand statistical properties of patient structure, and their effects on lesion detection, rather than the statistics of the images per se, so it was necessary to remove effects of the x-ray imaging and film digitizing procedures. Work is based on the log-exposure scale. Several algorithms were developed to estimate the breast image region corresponding to a constant thickness between the mammographic compression plates. Several analysis methods suggest that the tissue within that region, assuming second- order stationarity, is described by a power law spectrum of the form P(f) equals A/f(beta ), where f is radial spatial frequency and (beta) is about 3. There is no evidence of a flattening of the spectrum at low frequencies. Power law processes can have a variety statistical properties that seem surprising to an intuition gained using mildly random processes such as smoothed Gaussian or Poisson noise. Some of these will be mentioned. Since P(f) is approximately a 3rd order pole at zero frequency, spectral estimation is challenging.

Paper Details

Date Published: 21 May 1999
PDF: 12 pages
Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999); doi: 10.1117/12.348620
Show Author Affiliations
Arthur E. Burgess, Harvard Medical School (United States)


Published in SPIE Proceedings Vol. 3661:
Medical Imaging 1999: Image Processing
Kenneth M. Hanson, Editor(s)

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