DIRECTIONAL WEIGHT BASED CONTOURLET TRANSFORM DENOISING ALGORITHM FOR OCT IMAGE
Optical Coherence Tomography (OCT) imaging system has been widely used in biomedical field. However, the speckle noise in the OCT image prevents the application of this technology. The validity of existing contourlet-based denoising methods has been demonstrated. In the contourlet transform, the directional information contained by spatial domain is reflected in the corresponding sub-bands, while the noise is evenly distributed to each sub-band, resulting in a big difference among the coefficients' distribution of sub-bands. The traditional algorithms do not take these features into account, and only use uniform threshold shrinkage function to each sub-band, which limits the denoising effect. In this paper, a novel direction statistics approach is proposed to build a directional weight model in the spatial domain based on image gradient information to represent the effective edge information of different sub-bands, and this weight is introduced into threshold function for denoising. The experiments prove the effectiveness of this method. The proposed denoising framework is applied in contourlet soft threshold and bivariate threshold denoising algorithms for a large number of OCT images, and the results of these experiments show that the proposed algorithm effectively reduces noise while preferably preserves edge information.
contourlet transform optical coherence tomography (oct) image image denoising directional statistics of image