A Novel Diffusion-weighted Image Analysis System for Pediatric Metabolic Brain Diseases

被引：0
|
作者：
Mahmoodabadi, Sina Zarei [1 ]
Alirezaie, Javad [1 ,2 ]
Babyn, Paul [3 ]
机构：
[1] Ryerson Univ, Dep Elect & Comp Eng, Toronto, ON M5B 2K3, Canada
[2] KN Toosi Univ, Dept Elect Engn, Tehran 16315, Iran
[3] Hosp Sick Children, Dept Radiol, Toronto, ON M5G 1X8, Canada
来源：
2009 4TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING | 2009年
关键词：
Metabolic Brain Diseases; Diffusion Weighted Imaging; Exponential Apparent Diffusion Coefficients; Normalization; Wavelets; Fuzzy Relational Classifiers;
D O I：
暂无
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
The Diffusion Weighted Imaging (DWI) technique can be utilized to investigate a variety of diseases. We propose an automated system which assists the diagnosis of metabolic brain diseases clinically. In this study, DWI images are preprocessed and exponential Apparent Diffusion Coefficient (eADC) Images are produced. The eADC images are later brain extracted and normalized to a standard brain atlas. Subsequently, we utilized wavelets to denoise the eADC images. The images are rectified, thresholded and conspicuous abnormal regions are identified. Abnormal regions constitute the features that will be used by a fuzzy relational classifier in order to categorize the diseases. The sensitivity, positive predictivity and specificity of 60%, 60% and 93.33%, respectively in detecting metabolic brain diseases have been achieved.
引用
收藏
页码：715 / +
页数：2
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