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Background removal in dental panoramic X-ray images by the A-Trous multiresolution transform

Dental panoramic X-ray images are images having complex content, because several layers of tissue, bone, fat, etc. are superimposed. Nonuniform illumination, stemming from the X-ray source, gives extra modulation to the image, which causes spatially varying X-ray photon density. The interaction of the X-ray photons with the density of matter causes spatially coherent varying noise contribution. Many algorithms exist to compensate background effects, by pixel based or global methods. However, if the image is contaminated by a nonnegligible amount of noise, that is usually nonGaussian, the methods cannot approximate the background efficiently. In this paper, a dedicated approach for the removal of a multiplying background is presented using polynomial scaling and the A-Trous multiresolution transform. The new method uses a background image and a diagnostic image together to estimate the density of the diagnostic content. It assumes a locally Gaussian statistic scaled by a hidden factor, where the hidden factor represents the variance of the nonGaussian process of image generation. Because, the method also removes noise from the compound signal, a comparison to a standard denoising method is given. This approach has been tested on 50 images from a database of panoramic X-ray images where the results are cross validated by medical experts.