A Shearlet-based algorithm for quantum noise removal in low-dose CT images

Aguan Zhang; Huiqin Jiang; Ling Ma; Yumin Liu; Xiaopeng Yang

doi:10.1117/12.2216562

21 March 2016 A Shearlet-based algorithm for quantum noise removal in low-dose CT images

Aguan Zhang, Huiqin Jiang, Ling Ma, Yumin Liu, Xiaopeng Yang

Proceedings Volume 9784, Medical Imaging 2016: Image Processing; 97843O (2016) https://doi.org/10.1117/12.2216562
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Low-dose CT (LDCT) scanning is a potential way to reduce the radiation exposure of X-ray in the population. It is necessary to improve the quality of low-dose CT images. In this paper, we propose an effective algorithm for quantum noise removal in LDCT images using shearlet transform. Because the quantum noise can be simulated by Poisson process, we first transform the quantum noise by using anscombe variance stabilizing transform (VST), producing an approximately Gaussian noise with unitary variance. Second, the non-noise shearlet coefficients are obtained by adaptive hard-threshold processing in shearlet domain. Third, we reconstruct the de-noised image using the inverse shearlet transform. Finally, an anscombe inverse transform is applied to the de-noised image, which can produce the improved image. The main contribution is to combine the anscombe VST with the shearlet transform. By this way, edge coefficients and noise coefficients can be separated from high frequency sub-bands effectively. A number of experiments are performed over some LDCT images by using the proposed method. Both quantitative and visual results show that the proposed method can effectively reduce the quantum noise while enhancing the subtle details. It has certain value in clinical application.

Citation Download Citation

Aguan Zhang, Huiqin Jiang, Ling Ma, Yumin Liu, and Xiaopeng Yang "A Shearlet-based algorithm for quantum noise removal in low-dose CT images", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97843O (21 March 2016); https://doi.org/10.1117/12.2216562

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