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Data on Biomedicine and Biomedical Engineering Described by Researchers at Central South University (Outlier detection based on the neural network...

September 10, 2014



Data on Biomedicine and Biomedical Engineering Described by Researchers at Central South University (Outlier detection based on the neural network for tensor estimation)

By a News Reporter-Staff News Editor at Biotech Week -- Current study results on Biotechnology have been published. According to news reporting originating in Changsha, People's Republic of China, by NewsRx journalists, research stated, "Rationale and objectives: Diffusion weighted imaging (DWI) is always influenced by both thermal noise and spatially/temporally varying artifacts such as subject motion and cardiac pulsation. Motion artifacts are particularly prevalent, especially when scanning an uncooperative population with several disorders."

The news reporters obtained a quote from the research from Central South University, "Some motion between acquisitions can be corrected by co-registration approaches. However, automated and accurate motion outlier detection of brain DWIs is an integral component of the analysis and interpretation of tensor estimation. Many different and innovative methods have been proposed to improve upon this technology. In this study, we proposed a classifier work frame, which can classify DWIs as normal images or motion artifacts. The procedure contains the following stages: first, we used the wavelet transform to extract features from the original DWIs; second, the principle component analysis was used to reduce the features; third, the forward neural network (FNN) was employed to construct the classifier; fourth, a Rossler-based chaotic particle swarm optimization method was proposed to train the FNN; fifth, the cost matrix was determined as the false negative (FN) which was 10 times larger than the false position (FP); and finally, the K-fold cross validation was chosen to avoid overfitting. We applied this method on 60 DWI datasets, including 50 training datasets and 10 test datasets. The experimental results based on our DWI database showed that the proposed method can effectively extract the global feature from images and achieve better performance in tensor estimation by automatic unvoxelwise outlier rejection compared with manual and visual inspection, and previous voxelwise outlier rejection methods."

According to the news reporters, the research concluded: "We found that the motion artifact detection accuracy on both the training and test datasets was over 95.8%, while the computation time per DWI slice was only 0.0149 S. The proposed method could potentially remove the influence of unexpected motion artifacts in DWI acquisitions and should be applicable to other magnetic resonance imaging."

For more information on this research see: Outlier detection based on the neural network for tensor estimation. Biomedical Signal Processing and Control, 2014;13():148-156. Biomedical Signal Processing and Control can be contacted at: Elsevier Sci Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, Oxon, England. (Elsevier - www.elsevier.com; Biomedical Signal Processing and Control - www.elsevier.com/wps/product/cws_home/706718)

Our news correspondents report that additional information may be obtained by contacting X.Y. Zhang, Central South University, Xiangya Hosp, Natl Hepatobiliary & Enter Surg Res Center, Minist Hlth, Changsha 410008, Hunan, People's Republic of China (see also Biotechnology).

Keywords for this news article include: Changsha, People's Republic of China, Asia, Biotechnology, Neural Networks

Our reports deliver fact-based news of research and discoveries from around the world. Copyright 2014, NewsRx LLC


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Source: Biotech Week


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