Parallel Fuzzy Inference System (PFIS) based Medical Image Denoising

Abstract

Objective: To remove the noises occurred in the bio medical pictures with less computation time. Methods: An innovative technique called Parallel Fuzzy Inference System (PFIS) is introduced for image denoising in the medical images. This method takes input training images, process a dissimilar neighbourhood association between the center pixel and generates the fuzzy inference rules. These rules are distributed to the nodes for simultaneous execution. Then, the type-1 interval fuzzy set is submitted to the resultant defuzzifier module which will decode it into scalar value. If it is impulse pixel, the noise filter is used to reduce the noise. Results: The improvement of the PFIS technique is evaluated by using the medical images. PFIS method shows high efficiency when matched with the existing Type-2 Fuzzy Logic (TFL) based impulse detector for impulse noise removal. In the PFIS method, the fuzzy rules are generated analyzing the medical images and these rules are processed simultaneously. If the noise density is 75%, the Mean Squared Error (MSE) in PFIS is 59.48, the False Classification Ratio (FCR) is 3.25 and the computation time is 740 ms. According to the comparison and the results from the experiment shows that the proposed method has high efficiency. Conclusion: The findings demonstrate that the PFIS is presented and this method has high efficiency in terms of MSE, FCR and computation time.