Early detection of Alzheimer’s disease is critical for timely intervention, and neuroimaging biomarkers play a fundamental role in assessing structural brain changes. The Koedam visual scale is a widely used tool for evaluating parietal atrophy, particularly in early-onset AD. This study presents an automated approach to Koedam scale classification using T1-weighted MRI features and clustering techniques. The proposed method follows a structured pipeline, including skull stripping, noise reduction, bias field correction, and region of interest (ROI) selection. Brain tissue segmentation is performed using a probabilistic model-based approach, classifying image voxels into gray matter, white matter, and cerebrospinal fluid. Additionally, deformation fields derived from nonlinear image registration with a non-atrophied template are extracted to capture structural differences associated with atrophy. The strain tensor, derived from the displacement field, is computed to further characterize tissue deformation. A feature selection step is applied before clustering, where a Gaussian Mixture Model (GMM) clustering algorithm is used to categorize images into four Koedam atrophy levels, mimicking expert visual assessment. The method was evaluated on a dataset of 103 MRI images, demonstrating a clear differentiation between atrophy severity levels. The resulting clusters exhibited progressively increasing mean Root Mean Square displacement magnitude (RMSdm) values: 0.56±0.08 for cluster 0, 0.59±0.06 for cluster 1, 0.84±0.07 for cluster 2, and 1.09±0.14 for cluster 3. These findings indicate that the proposed approach effectively quantifies parietal atrophy, providing an objective and reproducible alternative to expert visual assessment.