Spectral transform-based nonlinear restoration of medical images: algorithms and comparative evaluation

In this paper, we derive algorithms for noise reduction and image enhancement using spectral amplitude estimation. The algorithms are based on a short space spectral analysis by either the DFT, the DCT or the Modulated Lapped Transform (MLT). We apply these algorithms to low-dose X-ray images acquired in a medical imaging modality called fluoroscopy. Giving moving images in real time, only low dose rates can be used to protect humans from extensive exposure. Low X-ray quantum counts associated with such low doses then result in considerable degradations of image quality through quantum noise (QN). Spectral-domain filtering allows specific tailoring of the algorithms to the two prominent properties of QN, viz. signal dependence and a lowpass shaped, nonwhite noise power spectrum. A comparison shows that the DFT performs best and even allows to detect orientation, while the DCT and MLT perform similarly to each other, with the MLT being least computationally demanding. The noise reduction achieved is about 5-6dB.