Noise is a natural property of medical imaging, and it commonly tends to diminish the image resolution as well as contrast, thus dropping the diagnostic rate of this imaging modality, there is a developing attentiveness in using noise reduction techniques in a variety of medical imaging applications. This paper presents a hybrid nonlinear filtering algorithm in which the proposed method has two stages. In the first stage, the rank-ordered sequence is used to decide whether a pixel is corrupted or not based on a decision measure which considers the differences of adjacent pixel values in the input image. In the second stage, the replacement is done by the weighted median value of uncorrupted pixels in the filte1ing window. The visual and experimental results show that the proposed filter can provide very high quality restored images with image detail preservation for various level noise density images.

Alzheimer’s disease, Denoising, MRI, Non – linear, Median filter

[1] Rejina A.S, M.Selin, A.Neela Madheswari "Performance Analysis of Nonlinear Decision Based Filter and Weighted Median ε-Filter for Salt and Pepper Noise Removal" IRACST - International Journal of Computer Science and Information Technology & Security (IJCSITS), ISSN: 2249-9555,Vol. 3, No.5, 2013.
[2] Raju Anitha, S.Jyothi, P.Ramesh Babu"Detection of Brain Abnormality for Alzheimer`s Disease Using Image Processing Techniques" https://www.researchgate.net/publication/305640527, January 2015.
[3] Benjamin Y.M.Kwan ” Impulse Noise Reduction in Brain Magnetic Resonance Imaging using Fuzzy Filters” World Academy of Science, Engineering and Technology 2011.
[4] Kamboj P. & Rani V.,“A Brief study of various noise models and filtering techniques,” Journal of Global Research in Computer Science, Vol. 4, No. 4, 2013.
[5] S. Shrestha, “Image Denoising Using New Adaptive Based Median Filter”, Signal & Image Processing: An International Journal (SIPIJ), vol. 5, no. 4, 2014.
[6] Ping Kuang, Lei Sun, “An Improved Two-Dimensional Multi-Level Median Filtering Algorithm” IEEE International Conference on Apperceiving Computing and Intelligence Analysis Proceeding, 2010.
[7] Zhu Youlian, Huang Cheng, “An Improved Median Filtering Algorithm Combined with Average Filtering” IEEE Third International Conference on Measuring Technology and Mechatronics Automation, 2011.
[8] Raghuram Kunsoth, Mantosh Biswas "Modified Decision Based Median Filter for Impulse Noise Removal" IEEE International conference on Wireless Communication, Signal Processing and Networking-WiSPNET, 2016.
[9] K. S. Srinivasan, and D. Ebenezer, ”A New Fast and Efficient Decision Based Algorithm for Removal of High Density Impulse Noises”, Signal Processing Letters, Vol. 14, No. 3, pp. 189-192, 2007.
[10] Aparna Rajesh Lokhande, Munmun Das, Dr.D.B.Salunke " Efficient Removal of Impulse Noise and Analyze Performance of Median Filter and Center Weighted Median Filter" International Journal of Engineering Science and Computing, 2016.
[11] Zhu Youlian, Huang Cheng, “An Improved Median Filtering Algorithm Combined with Average Filtering”, ICMTMA `11 Proceedings of Int. Conference on Measuring Technology and Mechatronics Automation, IEEE, vol1, pp 420-423, 2011.
[12] Z. Wang, A.C. Bovik, H.R. Sheikh, E.P. Simoncelli, Image quality assessment: from error visibility to structural similarity, IEEE Transaction on Image Processing vol. 13, No. 4, pp.600-611, 2004.
[13] Debdoot Sheet, Santanu ParI, Arindam Chakraborty , Jyotirmoy Chatterjee and Ajoy K. Ray t “Visual Importance Pooling for Image Quality Assessment of Despeckle Filters in Optical Coherence Tomography” IEEE International Conference on Systems in Medicine and Biology, 2010.