title: Evaluation of Alzheimer's disease by analysis of MR images using Objective Dialectical Classifiers as an alternative to ADC maps.
abstract: Alzheimer's disease is the most common cause of dementia, yet hard to diagnose precisely without invasive techniques, particularly at the onset of the disease. This work approaches image analysis and classification of synthetic multispectral images composed by diffusion-weighted (DW) magnetic resonance (MR) cerebral images for the evaluation of cerebrospinal fluid area and measuring the advance of Alzheimer's disease. A clinical 1.5 T MR imaging system was used to acquire all images presented. The classification methods are based on Objective Dialectical Classifiers, a new method based on Dialectics as defined in the Philosophy of Praxis. A 2-degree polynomial network with supervised training is used to generate the ground truth image. The classification results are used to improve the usual analysis of the apparent diffusion coefficient map.
authors.affiliation: Departamento de Engenharia Elétrica, Universidade Federal de Campina Grande, 58109-970 Paraíba, Brazil. wps@dsc.upe.br
authors.forename: Wellington P
authors.initials: WP
authors.lastname: Dos Santos
authors.forename: Francisco M
authors.initials: FM
authors.lastname: de Assis
authors.forename: Ricardo E
authors.initials: RE
authors.lastname: de Souza
authors.forename: Plinio B
authors.initials: PB
authors.lastname: Dos Santos Filho
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000473:pathology*; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology*; D016000:Cluster Analysis; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity