CT image denoising based on sparse representation using global dictionary

摘要：

　　Low-dose CT (LDCT) images tend to be severely degraded by mottle and streak-like noise,and how to enhance image quality under low-dose CT scanning has attracted more and more attention.This work aims to improve LDCT abdomen image quality through a dictionary learning based de-noising method and accelerate the training time at the same time.The proposed method suppresses noise through reconstructing the image use only one dictionary.Experimental results show that the proposed method is effective in suppressing noise while maintaining the diagnostic image details with much more less time.

关键词： Low-dose CT(LDCT) abdomen tumor preprocessing learning dictionary one dictionary

作者: Fei Yu Yang Chen Limin Luo

作者单位: Laboratory of Image Science and Technology, Southeast University, Nanjing, China Laboratory of Image Science and Technology, Southeast University, Nanjing, China; Centre de Recherch Laboratory of Image Science and Technology, Southeast University, Nanjing, China; Centre de Recherch

会议类型: 国际会议

会议名称: 2013 ICME International Conference on Complex Medical Engineering(2013 ICME复合医学工程国际会议)

会议地点: 北京

会议语种:英文

页码: 408-411

在线出版日期: 2013-05-25（万方平台首次上网日期，不代表论文的发表时间）