The neural system of Brain will help to exchange the information throughout the anatomical structure of human body. Alzheimer's disease (AD) is a progressive form of dementia that is caused by a monogenic accumulative condition that mostly impacts memory function in the human brain. Using MRI as the primary research tool, the primary objective of this work is to locate and categorize Alzheimer's disease biomarkers. Two different adaptive filtering strategies are utilized in order to eliminate the noise in MRI scans. In order to detect the segmentation of the Alzheimer's disease affected region, the TDWT-Fuzzy Set Theory has been proposed as a method. The Alzheimer's disease Neuroimaging Initiative (ADNI) database was used to evaluate the approach. This database has one hundred different datasets. The collection of data includes T1-weighted MR images in the sagittal plane, comprising 40 normal samples, 40 MCI samples, and 20 AD samples. There are 51 female samples and 49 male samples total. Their ages range anywhere from 57 to 95 years, with the average being 95. To remove the noise of MRI images of AD, the Hampel Identifier, Adaptive median filter and proposed method are used. The proposed method shows that better results when compared to the other images with (30%, 40% and 50%). The proposed segmentation algorithms performance evaluation of PSNR, SNR, MSE and MAE shows more than 53% than compared with Threshold, Region growing. When compared to previous image segmentation techniques, the suggested method performs better at identifying areas that are afflicted by Alzheimer's disease.

Alzheimer’s disease, DWT, Fuzzy set, Threshold, Region growing, Segmentation.