Noise Reduction for Cone-Beam Micro-CT by Fuzzy-Logic Based Non-Linear Filters

In Micro-CT, the smaller voxel requires much higher dose to achieve an adequate signal-to-noise ratio compared to clinical CT. Thus, it necessitates a method that reduces statistical noise in order to improve dose efficiency. We have developed a fuzzy-logic based non-linear Gaussian (NLG) filtering for smoothing projection data before reconstructed. We propose a fuzzy-logic based edge detector to determining the degree of smoothing. Both the fuzzy membership function and fuzzy rules are defined in such a way that the performance of the edge detector is optimized in terms of its robustness to noise. Additionally, we use NLG filter to obtain the filtering weights depending not only on difference in pixel locations but also on difference in density values. Several filtering steps with different NLG parameters can effectively reduce noise without losing structure information. Results with phantom and animal data suggested that our method is useful for the image quality improvement and radiation dose reduction of the micro-CT.