Single NMR image super-resolution based on extreme learning machine. Issue 10 (October 2016)

• Record Type: Journal Article
• Authors: Wang, Zhiqiong; Xin, Junchang; Wang, Zhongyang; Tian, Shuo; Qiu, Xuejun
• Is Part Of: Physica medica. Volume 32:Issue 10(2016)
• Page Start: 1331
• Page End: 1338
• Publication Date: 2016-10
• Subjects: Super-resolution -- Single image -- NMR -- Extreme learning machine; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2016.09.001
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: The NMR image niose adding and down sampling is established to reduce the impact of noise. A model based on ELM is established to enhance the resolution of NMR image. The efficiency and effectiveness of the method is verified by extensive experiments. Abstract: Introduction: 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 and materials: 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. Discussion: 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.