Journal
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON FRONTIERS OF INTELLIGENT COMPUTING: THEORY AND APPLICATIONS (FICTA) 2013
Volume 247, Issue -, Pages 129-136Publisher
SPRINGER-VERLAG BERLIN
DOI: 10.1007/978-3-319-02931-3_16
Keywords
Alzheimer's disease; Mild Cognitive Impairment; No Cognitive impairment; Artificial Neural network; Magnetic Resonance Imaging
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Alzheimer's disease (AD) is the most common cause of dementia among people aged 60 years and older. Mild Cognitive impairment (MCI) is a pre-dementia condition that has been shown to have a high likelihood of progression to AD. In this prospective study evaluate the accuracy of the GM and CSF volumetry to help distinguish between patients with AD and MCI and subjects with elderly controls. This study we explored the ability of BP-ANN identify the structural changes of Grey Matter (GM), White Matter (WM) and Cerebrospinal fluid (CSF) in different groups using real MR images. The proposed approach employs morphological operations used for skull stripping and gabor filter for feature extraction. In these results we report a statistically significant trend towards accelerated GM volume loss in the MCI group compared to the NCI and AD from the MCI. We report the results of the classification accuracies on both training and test images are up to 96%.
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