1. Speckle noise reduction of multi-frame optical coherence tomography data using multi-linear principal component analysis

    Speckle noise reduction of multi-frame optical coherence tomography data using multi-linear principal component analysis

    Optical coherence tomography (OCT) is an important interferometric diagnostic technique extensively applied in medical sciences. However, OCT images inevitably suffer from speckle noise, which reduces the accuracy of the diagnosis of ocular diseases. To deal with this problem, a speckle noise reduction method based on multi-linear principal component analysis (MPCA) is presented to denoise multi-frame OCT data. To well preserve local image features, nonlocal similar 3D blocks extracted from the data are first grouped using k-means++ clustering method. MPCA transform is then performed on each group and the transform coefficients are shrunk to remove speckle noise. Finally, the filtered OCT ...

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