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MR Weighted Image Discrimination by Artificial Intelligence

Authors
 Giljae Lee  ;  Gyehwan Jin  ;  Hwunjae Lee  ;  Jaeeun Jung 
Citation
 ScholarGen Journal of Medical Imaging, Vol.3(11) : 33, 2019-12 
Journal Title
 ScholarGen Journal of Medical Imaging 
ISSN
 2635-4608 
Issue Date
2019-12
Keywords
Image processing ; Discrete Wavelet Transform ; MR pulse sequence ; T2 Weighted Image ; MR Molecular Imaging ; Magnetic nanoparticles
Abstract
In this study, we proposed a method of learning neural networks by optimizing neural network input parameters to discern MRI-weighted images. To this end, we segmented the weighting domain of MRI. In feature extraction, the original image and segmented image were extracted by DWT, respectively. A neural network was trained by inputting extracted feature values. As a result of the experiment, it was found that the R-value of the segmented image is closer to 1 than the original image. The reason is that the images obtained by segmenting the areas of the weighted parts already have similarities. Also, it was found that the similarity between T1 and T2 weighted images is high in the same combination, and the similarity is relatively low in different weighted images. The most important issue in medical imaging is ensuring the confidence of radiologists using artificial intelligence. To solve this problem, it is of utmost importance that the algorithm developer and radiological A neural network was trained by inputting extracted feature values. As a result of the experiment, it was found that the R-value of the segmented image is closer to 1 than the original image. The reason is that the images obtained by segmenting the areas of the weighted parts already have similarities. Also, it was found that the similarity between T1 and T2 weighted images is high in the same combination, and the similarity is relatively low in different weighted images. The most important issue in medical imaging is ensuring the confidence of radiologists using artificial intelligence. To solve this problem, it is of utmost importance that the algorithm developer and radiological A neural network was trained by inputting extracted feature values. As a result of the experiment, it was found that the R-value of the segmented image is closer to 1 than the original image. The reason is that the images obtained by segmenting the areas of the weighted parts already have similarities. Also, it was found that the similarity between T1 and T2 weighted images is high in the same combination, and the similarity is relatively low in different weighted images. The most important issue in medical imaging is ensuring the confidence of radiologists using artificial intelligence. To solve this problem, it is of utmost importance that the algorithm developer and radiological technologist work together to provide a solution that is integrated with the radiologist's workflow.
Files in This Item:
T201906607.pdf Download
DOI
10.31916/sjmi2019-01-02
Appears in Collections:
5. Research Institutes (연구소) > Institute for Medical Convergence (연의-생공연 메디컬융합연구소) > 1. Journal Papers
Yonsei Authors
Lee, Hwun Jae(이훈재)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/179830
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