Study on compressed sensing reconstruction algorithm of medical image based on curvelet transform of image block

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• 作者：Xiaoping JiangAuthor Vitae ; Hao Ding ; ^{dinghaoceie@163.com}Author Vitae ; Hua ZhangAuthor Vitae ; Chenghua LiAuthor Vitae
• 关键词：Wavelet transform ; Curvelet transform ; Compressed sensing ; Regularization parameter ; Sampling frequency ; SNR
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：12 January 2017
• 年：2017
• 卷：220
• 期：Complete
• 页码：191-198
• 全文大小：2306 K
• 卷排序：220

文摘

Traditional MRI technology may easily generate artifact due to slow imaging speed, therefore, MRI has low imagining quality and over-long sampling duration. Since wavelet transform cannot achieve the best approximation, image block theory is introduced in compressed sensing image reconstruction. In combination of the advantage of curvelet transform – it is suitable for expressing edge detail information and curve information, curvelet transform is utilized to conduct sparse representation of MRI image and proposed compressed sensing reconstruction algorithm of MRI image based on curvelet transform of image block. Signal to Noise Ratio (SNR), Relative L2 norm error (RLNE) and matching degree served as the evaluation indexes, and 4 groups of experiments about the influence of noise-free image, noised image, different sampling frequencies and different regularization parameters on the quality of reconstructed image were done. The results show that during image reconstruction, the algorithm proposed in this paper is superior to SIDCT and PBDCT in terms of three evaluation indexes. Besides, the algorithm owns strong ability to resist noise and good effects on keeping image detail and edge.