Brain MR image classification for Alzheimer's disease diagnosis using structural hippocampal asymmetrical attributes from directional 3-D log-Gabor filter responses

Autor(es)

Poloni, Katia M. ^[1] ; de Oliveira, Italo A. Duarte ^[1] ; Tam, Roger ^[2] ; Ferrari, Ricardo J. ^[1] ; Initi, Alzheimers Dis Neuroimaging

Afiliação do(s) autor(es)

^[1] Univ Fed Sao Carlos, Dept Comp, Rod Washington Luis, Km 235, BR-13565905 Sao Carlos, SP - Brazil

^[2] Univ British Columbia, Sch Biomed Engn, Dept Radiol, Djavad Mowafaghian Ctr Brain Hlth, 2215 Wesbrook Mall, Vancouver, BC V6T 2B5 - Canada

• Tipo de documento: Artigo Científico
• Fonte: Neurocomputing; v. 419, p. 126-135, JAN 2 2021.
• Citações Web of Science: 1

Resumo

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative condition whose development is characterized by lateralized brain atrophies. In AD, the hippocampus is the first brain structure to present atrophy, which, although to a lesser extent, is also a precursor to the broader asymmetrical development of the human brain. Structural magnetic resonance (MR) imaging is capable of detecting the disease-induced anatomical changes in the brain, thus aiding the diagnosis of AD. MR image attributes extracted from the hippocampal regions are commonly used for the AD classification task. However, most of the published methods do not explore hippocampal asymmetries for image classification. In this study, we propose a new technique for performing the classification of MR images for AD using only hippocampal asymmetrical attributes. By using the new proposed asymmetry index (AI), we assessed the attributes and the ones that passed the analysis of variance test, i.e., showing statistically mean differences among the classes (CN, MCI, and AD), were selected for classification. As a result of our study, the statistical analysis of our AI has shown a significant increase in hippocampal asymmetry as disease progress (CN < MCI < AD). Moreover, for the classification using clinical MR images, we obtained accuracy values of 69.44% and 82.59%; and AUC values of 0.76 and 0.9 for CN x MCI and CN x AD, respectively. Last, we found the results of our asymmetry analysis consistent with other statistical assessments and our classification results, using only asymmetry attributes comparable to (or even higher than) existing hippocampus studies. (c) 2020 Published by Elsevier B.V. (AU)

• Processo FAPESP: 18/09972-9 - Detecção e análise de assimetrias estruturais hipocampais em imagens de ressonância magnética com aplicação no auxílio ao diagnóstico da Doença de Alzheimer
• Beneficiário: Italo Antonio Duarte de Oliveira
• Modalidade de apoio: Bolsas no Brasil - Iniciação Científica
• Processo FAPESP: 18/06049-5 - Esquema computacional automático para a detecção, identificação e classificação de alterações estruturais cerebrais em imagens de ressonância magnética para auxílio ao diagnóstico de pacientes com comprometimento cognitivo leve e Doença de Alzheimer leve
• Beneficiário: Katia Maria Poloni
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 18/08826-9 - Desenvolvimento de técnicas de engenharia de características e aprendizagem profunda aplicadas à classificação de imagens de ressonância magnética nas classes envelhecimento cognitivo saudável, comprometimento cognitivo leve e Doença de Alzheimer
• Beneficiário: Ricardo José Ferrari
• Modalidade de apoio: Auxílio à Pesquisa - Regular