Adaptive Fuzzy Enhancement Algorithm of Surface Image based on Local Discrimination via Grey Entropy

Image artifacts and noise reduction algorithm for cone-beam computed tomography with low-signal projections

This study aims to investigate and test a new image reconstruction algorithm applying to the low-signal projections to generate high quality images by reducing the artifacts and noise in the cone-beam computed tomography (CBCT). For the low-signal and noisy projections, a multiple sampling method is first utilized in projection domain to suppress environmental noise, which guarantees the accuracy of the data for reconstruction, simultaneously. Next, a fuzzy entropy based method with block matching 3D (BM3D) filtering algorithm is employed to improve the image quality to reduce artifacts and noise in image domain. Then, simulation studies on polychromatic spectrum were performed to evaluate the performance of the proposed new algorithm. Study results demonstrated significant improvement in the signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) of the images reconstructed using the new algorithm. SNRs and CNRs of the new images were averagely 40% and 20% higher than those of the previous images reconstructed using the traditional algorithms, respectively. As a result, since the new image reconstruction algorithm effectively reduced the artifacts and noise, and produced images with better contour and grayscale distribution, it has the potential to improve image quality using the original CBCT data with the low and missing signals.