Noise equalization for detection of microcalcification clusters in direct digital mammogram images
Abstract
Equalizing image noise is shown to be an important step in the automatic detection of microcalcifications in digital mammography. This study extends a well established film-screen noise equalization scheme developed by Veldkamp et al. for application to full-field digital mammogram (FFDM) images. A simple noise model is determined based on the assumption that quantum noise is dominant in direct digital X-ray imaging. Estimation of the noise as a function of the gray level is improved by calculating the noise statistics using a truncated distribution method. Experimental support for the quantum noise assumption is presented for a set of step wedge phantom images. Performance of the noise equalization technique is also tested as a preprocessing stage to a microcalcification detection scheme. It is shown that the square root model based approach which FFDM allows leads to a robust estimation of the high frequency image noise. This provides better microcalcification detection performance when compared to the film-screen noise equalization method developed by Veldkamp. Substantially better results are obtained than when noise equalization is omitted. A database of 124 direct digital mammogram images containing 28 microcalcification clusters was used for evaluation of the method.
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Evidence Hub reads this as a clinical trial, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence.
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