Medical imaging plays an essential role in diverse medical diagnosis processes and can be used to recognise an early detection of Alzheimer's disease. Medical image segmentation helps us to pull out precious knowledge from a large quantity of medical image data. For better image segmentation, further phases must be processed in order to succeed in reading medical images clearly and to extract the exact stage of Alzheimer's disease. Such a step, reducing noise from MRI. Gaussian noise and Salt and pepper noise are examples of noises present in images. There are many denoising techniques, like the filtering domain and especially the median filter that proves its effectiveness in reducing the Salt and pepper noise. In this paper, we propose an extension work of the median filter method. In this paper noisy pixels are detected using the occurrence of intensity values 0's and 255's and uses 3x3 size windows to have better information about the center neighbors. Tested on noise in the range 20% to 80% and applied on Magnetic Resonance Imaging data set from the Alzheimer's disease Neuroimaging Initiative database. The results demonstrate the effectiveness of our algorithm compared to the standard and other improvements.