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Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2017 Jun 1;34(3):431-438. doi: 10.7507/1001-5515.201603061.

[Detection algorithm of amyloid β-protein deposition in magnetic resonance image based on pixel feature learning method].

[Article in Chinese; Abstract available in Chinese from the publisher]

Author information

1
College of Communication Engineering, Chongqing University, Chongqing 400044, P.R.China.
2
College of Communication Engineering, Chongqing University, Chongqing 400044, P.R.China;Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing 400044, P.R.China;Department of Medical Image, College of Biomedical Engineering, Third Military Medical University, Chongqing 400038, P.R.China.yongmingli@cqu.edu.cn.
3
Department of Medical Image, College of Biomedical Engineering, Third Military Medical University, Chongqing 400038, P.R.China.

Abstract

in English, Chinese

Amyloid β-protein (Aβ) deposition is an important prevention and treatment target for Alzheimer's disease (AD), and early detection of Aβ deposition in the brain is the key to early diagnosis of AD. Magnetic resonance imaging (MRI) is the perfect imaging technology for the clinical diagnosis of AD, but it cannot display the plaque deposition directly. In this paper, based on two feature selection modes-filter and wrapper, chain-like agent genetic algorithm (CAGA), principal component analysis (PCA), support vector machine (SVM) and random forest (RF), we designed six kinds of feature learning classification algorithms to detect the information (distribution) of Aβ deposition through magnetic resonance image pixels selection. Firstly, we segmented the brain region from brain MR images. Secondly, we extracted the pixels in the segmented brain region as a feature vector (features) according to rows. Thirdly, we conducted feature learning on the extracted features, and obtained the final optimal feature subset by voting mechanism. Finally, using the final optimal selected features, we could find and mark the corresponding pixels on the MR images to show the information about Aβ plaque deposition by elastic mapping. According to the experimental results, the proposed pixel features learning methods in this paper could extract and reflect Aβ plaque deposition, and the best classification accuracy could be as high as 80%, thereby showing the effectiveness of the methods. The proposed methods can precisely detect the information of the Aβ plaque deposition, thereby being helpful for improving classification accuracy of diagnosis of AD.

KEYWORDS:

Alzheimer’s disease; amyloid β-protein deposition; detection; magnetic resonance imaging; pixel feature learning

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