Single NMR image super-resolution based on extreme learning machine.
BACKGROUND
The performance limitation of MRI equipment and higher resolution demand of NMR images from radiologists have formed a strong contrast. Therefore, it is important to study the super resolution algorithm suitable for NMR images, using low costs software to replace the expensive equipment-updating.


METHODS
Firstly, a series of NMR images are obtained from original NMR images with original noise to the lowest resolution images with the highest noise. Then, based on extreme learning machine, the mapping relation model is constructed from lower resolution NMR images with higher noise to higher resolution NMR images with lower noise in each pair of adjacent images in the obtained image sequence. Finally, the optimal mapping model is established by the ensemble way to reconstruct the higher resolution NMR images with lower noise on the basis of original resolution NMR images with original noise. Experiments are carried out by 990111 NMR brain images in datasets NITRC, REMBRANDT, RIDER NEURO MRI, TCGA-GBM and TCGA-LGG.


RESULTS
The performance of proposed method is compared with three approaches through 7 indexes, and the experimental results show that our proposed method has a significant improvement.


CONCLUSIONS
Since our method considers the influence of the noise, it has 20% higher in Peak-Signal-to-Noise-Ratio comparison. As our method is sensitive to details, and has a better characteristic retention, it has higher image quality upgrade of 15% in the additional evaluation. Finally, since extreme learning machine has a celerity learning speed, our method is 46.1% faster.
Sino-Dutch Biomedical & Information Engineering School, Northeastern University, China.
Zhiqiong
Z
Wang
School of Computer Science & Engineering, Northeastern University, China. Electronic address: xinjunchang@cse.neu.edu.cn.
Junchang
J
Xin
Sino-Dutch Biomedical & Information Engineering School, Northeastern University, China.
Zhongyang
Z
Wang
Sino-Dutch Biomedical & Information Engineering School, Northeastern University, China.
Shuo
S
Tian
Beijing Daheng Medical Equipment Co., Ltd., Chinese Academy of Sciences, China.
Xuejun
X
Qiu
D000465:Algorithms; D055592:Biophysical Phenomena; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D019047:Phantoms, Imaging; D059629:Signal-To-Noise Ratio
Extreme learning machine; NMR; Single image; Super-resolution