title: [Bi-modality image classification based on independent component analysis].
abstract: We in the present research proposed a classification method that applied infomax independent component analysis (ICA) to respectively extract single modality features of structural magnetic resonance imaging (sMRI) and positron emission tomography (PET). And then we combined these two features by using a method of weight combination. We found that the present method was able to improve the accurate diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Compared AD to healthy controls (HC): the study achieved a classification accuracy of 93.75%, with a sensitivity of 100% and a specificity of 87.64%. Compared MCI to HC: classification accuracy was 89.35%, with a sensitivity of 81.85% and a specificity of 99.36%. The experimental results showed that the bi-modality method performed better than the individual modality in comparison to classification accuracy.
authors.forename: Jie
authors.initials: J
authors.lastname: Guan
authors.forename: Wenlu
authors.initials: W
authors.lastname: Yang
mesh_terms: D000544:Alzheimer Disease / Q000175:diagnosis*; D016022:Case-Control Studies; D060825:Cognitive Dysfunction / Q000175:diagnosis*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging; D049268:Positron-Emission Tomography; D012680:Sensitivity and Specificity