Improved Brain MRI Segmentation for Early Detection of Alzheimer's Disease using Overlaying Analysis and Thresholding

Alzheimer's disease (AD) with memory loss and cognitive decline as symptoms of the neuro-degenerative condition, damages the brain. The novel approach is proposed for detecting regions of the brain affected by AD using overlaying analysis and thresholding techniques. The current overlaying analysis methods lack accurate thresholding, which leads to a loss of pixel data, indicating highly varied segregation of brain regions. The usage of efficiently identifying disparities in pixel values between multiple digital images through computational analysis is highlighted in the proposed work. We also discuss the challenges and limitations of current image analysis methods, including issues related to misaligned scans and under-thresholding. A new function to find absolute pixel differences on brain Magnetic Resonance Imaging (MRI) scans, segregate the brain into multiple affected regions, and identify those affected by Very Mild Dementia Alzheimer's is applied. The segregated regions are then overlapped and compared with healthy brain regions to observe differences in structural and functional properties. The approach can accurately identify affected regions of the brain and provide valuable information for the diagnosis and prognosis of AD. The model contributes to the ongoing efforts to develop effective and non-invasive methods for detecting and tracking the progression of AD and highlights the potential of observing MRI scan differences using overlapping analysis as a powerful tool in neuroimaging research.