Optical coherence tomography (OCT) is a micrometer-resolution, cross-sectional imaging modality for biological tissue. It has been widely applied for retinal imaging in ophthalmology. However, the large speckle noise affects the analysis of OCT retinal images and their diagnostic utility. In this article, we present a new speckle reduction algorithm for 3D OCT images. The OCT speckle noise is approximated as Poission distribution, which is difficult to be removed for its signal-dependent characteristic. Thus our algorithm is consisted by two steps: first, a variance-stabilizing transformation, named Anscombe transformation, is applied to redistribute the multiplicative speckle noise into an additive Gaussian noise; then the transformed data is decomposed and filtered in 3D Shearlet domain, which provides better representation of the edge information of the retinal layers than wavelet and curvelet. The proposed method is evaluated through the three parameters using high-definition B-scans as the ground truth. Quantitative experimental results show that our method gives out the best evaluation parameters, and highest edge contrast, compared with state-of-the-art OCT denoising algorithms.