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| Noise Reduction for Low-dose CT Sinogram Based on Fuzzy Entropy |
| Liu Yi Zhang Quan Gui Zhi-guo |
| National Key Laboratory for Electronic Measurement Technology, North University of China, Taiyuan 030051, China |
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Abstract Low-dose Computed Tomography (CT) is widely used in modern medical practice for its advantage on reducing the radiation dose to patients. However, excessive quantum noise is present in low dose X-ray imaging along with the decrease of the radiation dose; thus, there are obvious streak-like artifacts in reconstructed images. For this problem, an adaptive restoration algorithm based on local fuzzy entropy is proposed in this paper. This new algorithm modifies the statistical information based anisotropic filter, distinguishing edges and smooth areas by a local fuzzy entropy. The new diffusion model can effectively control the diffusion degree, thus improve greatly the diffusion rate to achieve the purpose of rapid recovery of the projection data. Simulation results show that higher signal-to-noise ratio reconstructed images can be obtained by the new adaptive diffusion algorithm. In addition, compared with conventional algorithm, the proposed algorithm shortens processing time in projection domain and thereby reduces the hazards of radiation to patients.
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Received: 08 October 2012
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Corresponding Authors:
Gui Zhi-guo
E-mail: gzgtg@163.com
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2013, Vol. 35 
