Directional Weight Based Contourlet Transform Denoising Algorithm for Oct Image

Abstract

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.

Nondestructive Early Detection of Bruising in Pear Fruit Using Optical Coherence Tomography

“…Before separation, threshold denoising was performed to suppress noise disturbance with a threshold of  + 7, where  and  are the grayscale mean and variance from the top 20 rows in the background, respectively (Dong et al, 2013). After denoising, the boundary between the target and background was easily identified by determining the top non-zero pixels at each column in an OCT image.…”

Graph wavelet transform for image texture classification

“…Texture is a region property in an image, which is characterized with the intensities and relationships among pixels. Therefore, the texture features, that describe the relationships among the pixel intensities have been demonstrated power discriminative performance in texture classification, such as GLCM, Markov random field, LBP, some variants of the LBP [32], contourlet transform [33,34] and wavelet transforms. Although the traditional wavelet transforms have been successfully applied in texture feature extraction, it only implicitly considers the local structure of a texture by using the filter banks.…”