Performance of nonlinear methods in medical image restoration

In coherent image acquisition systems, occurrence of speckle noise is a common phenomenon that is hard to remove without degrading the original image. Conventional linear filtering processes fail to remove this noise and improve the signal to noise ratio without degrading the image. Some statistical filtering processes like the Wiener filter and the Local Linear Minimum Mean Squared Estimator (LLMMSE) filter have been applied with limited success. Nonlinear filtering processes such as the morphological filters are able to reduce some of this inherently associated noise with less degradation. A new approach to non-linear filtering that is capable of removing speckle noise without noticeable degradation is presented in this paper. This approach is based on an adaptive fuzzy leader clustering network known as AFLC. AFLC is a neuro-fuzzy clustering algorithm that can be used to cluster noise pixels in the image separately. After the clustering process is completed, a search is performed throughout the image to localize the noise pixels and to eliminate them using a spatial technique similar to the median filter. The results achieved by this process have been compared with the results from the traditional median filter, the LLMMSE filter, and the connectivity-preserving morphological filter demonstrating superior performance of AFLC in removing speckle noise.