Classificação da doença de Alzheimer baseada em graph kernels construídos a partir de atributos de textura 3D de imagens de ressonância magnética

• Ano de defesa: 2022
• Autor(a) principal: Mendonça, Lucas José Cruz de
• Orientador(a): Ferrari, Ricardo José
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos; Câmpus São Carlos
• Programa de Pós-Graduação: Programa de Pós-Graduação em Ciência da Computação - PPGCC
• Palavras-chave em Português: Doença de Alzheimer; Classificação de grafos; Extração de textura; Imagem de ressonância magnética
• Palavras-chave em Inglês: Alzheimer's disease; Graphs-based classification; Texture extraction; Magnetic resonance image; Graph kernel
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufscar.br/handle/20.500.14289/15789

Alzheimer's disease (AD) is neurodegenerative and characterized by cognitive and behavioral impairment. Mild cognitive impairment (MCI) is a relatively broad clinical condition involving a slight memory deficit, which in many cases represents a transitional state between a cognitively normal (CN) condition and AD. Many published studies restrict their analyses to searching for structural changes caused by the disease in a few particular regions of the brain.. Currently, the studies are looking for new AD biomarkers using multiple brain regions and focusing on subtle texture changes in the image. Therefore, this study proposes a new technique for MR image classification in AD diagnosis using graph kernels constructed from texture features extracted from 3D Structural magnetic resonance (sMR) images. In our proposed method we first segment the brain images into multiple regions with the FreeSurfer. Then, we extract 22 texture features and defined the graph node attributes as the probability distributions of the extracted features. Next, for each texture feature we build a graph and define its edge weights as the distances between pairs of node attributes using three metrics. After that, we use a threshold-based approach for graph edges removal. Finally, we perform graph classification using Support Vector Machines (SVMs) with two graph-kernels. Results of our method have shown better performances for the CN×AD (AUC=0.92) and CN×MCI (AUC=0.81) classifications, and worse for MCI×AD (AUC=0.78). This trend is consistent with other published results and makes sense if we consider the concept of Alzheimer's disease continuum from pathophysiological, biomarker and clinical perspectives. Besides allowing the use of different texture attributes for the diagnosis of AD, our method uses the graph-kernel approach to represent texture features from different regions of the brain image, which considerably facilitates the image classification task via SVMs. Our results were promising when compared to the state-of-the-art in graph-based AD classification.