Removal of random-valued impulse noise from Cerenkov luminescence images

Cerenkov luminescence imaging(CLI) is an emerging molecular imaging technology able to optically visualize radioactive decay signals from medical isotopes and has found wide application in tumor diagnose, cancer therapy, drug development, intraoperative guidance, and so on. When Cerenkov luminescence data are collected, the high-energy particles from the radioactive nucleus will be detected by the sensitive CCD camera and lead to impulse noise. To suppress the impulse noise and improve the contrast of the useful signal to the background, the detection-based fuzzy switching median filtering framework is proposed in this paper. Several experiments were conducted respectively to investigate the statistical feature of the noise and to evaluate the performance of the proposed noise removal framework. The results show that the signal-to-noise ratio is improved after noise elimination. The proposed filtering framework outperforms the classical median filter in terms of root mean squared error and the structural similarity index. It also preserves the maximum value and the mean value in the regions of interest better than the median filter does. In addition, compared with the FLICMCDD algorithm, the proposed method works much faster while getting similar results. Graphical abstract.