A novel algorithm for multiplicative speckle noise reduction in ex vivo human brain OCT images

NeuroImage

Caroline Magnain¹  Bruce Fischl²  Morgan Fogarty²  David A. Boas³  Divya Varadarajan⁴  Hui Wang⁵ 

[1] Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA;Radiology, Harvard Medical School, Boston, MA 02115, USA;Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA;Corresponding author at: Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA.;Imaging Science Program, Washington University McKelvey School of Engineering, St. Louis, MO 63130, USA;

关键词: Speckle;    Optical coherence tomography;    Multiplicative noise;    Artifact correction;    Majorize minimize;    Gamma distribution;   

DOI  :  

来源: DOAJ

【 摘 要 】

Optical coherence tomography (OCT) images of ex vivo human brain tissue are corrupted by multiplicative speckle noise that degrades the contrast to noise ratio (CNR) of microstructural compartments. This work proposes a novel algorithm to reduce noise corruption in OCT images that minimizes the penalized negative log likelihood of gamma distributed speckle noise. The proposed method is formulated as a majorize-minimize problem that reduces to solving an iterative regularized least squares optimization. We demonstrate the usefulness of the proposed method by removing speckle in simulated data, phantom data and real OCT images of human brain tissue. We compare the proposed method with state of the art filtering and non-local means based denoising methods. We demonstrate that our approach removes speckle accurately, improves CNR between different tissue types and better preserves small features and edges in human brain tissue.

【 授权许可】

Unknown