Article,
An Efficient Technique of De-Noising Medical Images using Neural Network and Fuzzy -A Review
International Journal of Innovative Science and Modern Engineering (IJISME), 1 (4): 66-68 (March 2013)
Abstract
Medical imaging technology is becoming an important component of large number of applications such as diagnosis, research, and treatment. Medical images like X-Ray, CT, MRI, PET and SPECT have minute information about heart brain and nerves. These images need to be accurate and free from noise. Noise reduction plays an important role in medical imaging. There are various methods of noise removal such as filters, wavelets and thresholding based on wavelets. Although these methods produced good results but still have some limitations. Considering and analyzing the limitations of the previous methods our research presents neural networks and fuzzy as an efficient and robust tool for noise reduction. In our research we use BPNN as the learning algorithm which follows the supervised learning and fuzzy. The proposed research use both mean and median statistical functions for calculating the output pixels of training patterns of the neural network and fuzzy provide promising results in terms of PSNR and MSE. The work focuses on study and performance evaluation of these categories using MATLAB 7.14.
