An Efficient Technique of De-Noising Medical Images using Neural Network and Fuzzy -A Review
Avisha Sharma1, Sanyam Anand2
1Avisha Sharma, Computer Science And Engineering, Lovely Professional University, Phagwara (Punjab), India.
2Sanyam Anand, Assistant Professor of Computer Science and Engineering, Lovely Professional University, Phagwara (Punjab), India.
Manuscript received on March 05, 2013. | Revised Manuscript received on March 11, 2013. | Manuscript published on March 15, 2013. | PP: 66-68 | Volume-1 Issue-4, March 2013. | Retrieval Number: D0196031413/2013©BEIESP
Open Access | Ethics and Policies | Cite
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Neural Network, Image De-noising, BPNN, PSNR, Fuzzy Logic.