Noise estimation in panoramic x-ray images: an application analysis approach

This paper presents an appropriate approach for the robust estimation of the noise statistics in dental panoramic X-ray images. To achieve maximum image quality after denoising, a semi-empirical scatter model is presented, leading to a local adaptive Gaussian scale mixture (GSM) model. State of the art methods use multiscale filtering of images to reduce the irrelevant part of information, based on generic estimation of noise. The usual assumption of a distribution of Gaussian and Poisson statistics only leads to overestimation of the noise variance in regions of low intensity (small photon counts), but to underestimation in regions of high intensity and therefore to non-optimal results. The analysis approach is tested on a database of 50 panoramic X-ray images and the results are cross-validated by medical experts. It is shown that the local standard deviation (SDEV) in images, stemming from homogeneous phantoms (AI, PMMA), follows a generalized Nakagami distribution (GND). The heavily tailed distribution is not covered entirely by the GND. The error density function, is hypothesized to stem from scatter-glare, degrading the image. A beam stop method, for estimation of the scatter-glare amount, verifies that hypothesis. Finally, the application of the method for a phantom image, is shown with denoising results for comparison purpose, followed by the conclusion