Autor(es)

Oliveira, Yasmin Victoria ; Ferrari, Ricardo Jose

• Tipo de documento: Artigo Científico
• Fonte: 2025 IEEE 38TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS, CBMS; v. N/A, p. 7-pg., 2025-01-01.

Resumo

Early detection of Alzheimer's disease (AD) 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 decreasing mean Mini-Mental State Examination (MMSE) values: 22.91 +/- 4.98 for cluster 0, 22.07 +/- 3.60 for cluster 1, 20.76 +/- 3.47 for cluster 2, and 19.84 +/- 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. (AU)

• Processo FAPESP: 23/15916-2 - Automatização da Escala de Atrofia Parietal de Koedam na Doença de Alzheimer Utilizando Atributos de Imagens de Ressonância Magnética T1-w e Técnicas de Agrupamento
• Beneficiário: Yasmin Victoria Oliveira
• Modalidade de apoio: Bolsas no Brasil - Iniciação Científica
• Processo FAPESP: 23/08307-0 - Predição da conversão de comprometimento cognitivo leve em doença de Alzheimer usando engenharia de atributos e aprendizado profundo em imagens de ressonância magnética estrutural
• Beneficiário: Ricardo José Ferrari
• Modalidade de apoio: Auxílio à Pesquisa - Regular