ADAPTIVE NOISE EQUALIZATION AND RECOGNITION OF MICROCALCIFICATION CLUSTERS IN MAMMOGRAMS

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Abstract
A statistical method is described for detection of microcalcifications in digital mammograms. It is shown that the detection performance depends strongly on a preprocessing step, in which the images are rescaled to equalize image noise. A robust algorithm is proposed for rescaling, which can be used to determine a proper scale conversion from a phantom recording. The same algorithm, however, can also be applied to the image to be processed itself. Such an adaptive approach, in which noise characteristics are estimated from the image at hand, appeared to be the basis for far better results than could be obtained by using a fixed scale conversion. The method used for detection is based on Bayesian techniques. A random field model is used to model spatial relations between the labels in an iterative segmentation process. Results of an experimental study using a set of 65 mammographic images digitized at 2048×2048 are presented.

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