KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Applied Mathematics and Computational Science Program
Visual Computing Center (VCC)
Computer Science Program
Permanent link to this recordhttp://hdl.handle.net/10754/625719
MetadataShow full item record
AbstractOptical coherence tomography (OCT) is a non-invasive technique with a large array of applications in clinical imaging and biological tissue visualization. However, the presence of speckle noise affects the analysis of OCT images and their diagnostic utility. In this article, we introduce a new OCT denoising algorithm. The proposed method is founded on a numerical optimization framework based on maximum-a-posteriori estimate of the noise-free OCT image. It combines a novel speckle noise model, derived from local statistics of empirical spectral domain OCT (SD-OCT) data, with a Huber variant of total variation regularization for edge preservation. The proposed approach exhibits satisfying results in terms of speckle noise reduction as well as edge preservation, at reduced computational cost.
CitationLi M, Idoughi R, Choudhury B, Heidrich W (2017) Statistical model for OCT image denoising. Biomedical Optics Express 8: 3903. Available: http://dx.doi.org/10.1364/boe.8.003903.
SponsorsKing Abdullah University of Science and Technology (KAUST) (Visual Computing Center Competitive Funding). We would like to thank Prof. TorOve Leiknes and Luca Fortunato of Water Desalination and Reuse Center, KAUST for help with the OCT data acquisition. We also take this opportunity to thank the ThorLabs, Munich engineering team for their assistance with the native OCT file handling procedures. The authors would also like to thank Mohamed Aly for his valuable feedback on the project.
PublisherThe Optical Society
JournalBiomedical Optics Express