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Updated: Jul 19, 2026

Basics of Multivariate Analysis in Neuroimaging Data
Published on: July 24, 2010
Alzheimer's disease: new insight in assessing of amyloid plaques morphologies using multifractal geometry based on
Elshaimaa Amin1,2, Yasmina M Elgammal2, M A Zahran2
1Future Higher Institute of Engineering and Technology, Mansoura, Egypt.
Abstract:
Alzheimer's disease (AD) is a physical illness, which damages a person's brain; it is the most common cause of dementia. AD can be characterized by the formation of amyloid-beta (Aβ) deposits. They exhibit diverse morphologies that range from diffuse to dense-core plaques. Most of the histological images cannot be described precisely by traditional geometry or methods. Therefore, this study aims to employ multifractal geometry in assessing and classifying amyloid plaque morphologies. The classification process is based on extracting the most descriptive features related to the amyloid-beta (Aβ) deposits using the Naive Bayes classifier. To eliminate the less important features, the Random Forest algorithm has been used. The proposed methodology has achieved an accuracy of 99%, sensitivity of 100%, and specificity of 98.5%. This study employed a new dataset that had not been widely used before.
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