Adaptive anisotropic diffusion for noise reduction of phase images in Fourier domain Doppler optical coherence tomography

Both speckle reduction and contrast enhancement for optical coherence tomography via sequential optimization in the logarithmic domain based on a refined Retinex model

Optical coherence tomography (OCT) image enhancement is a challenging task because speckle reduction and contrast enhancement need to be addressed simultaneously and effectively. We present a refined Retinex model for guidance in improving the performance of enhancing OCT images accompanied by speckle noise; a physical explanation is provided. Based on this model, we establish two sequential optimization functions in the logarithmic domain for speckle reduction and contrast enhancement, respectively. More specifically, we obtain the despeckled image of an entire OCT image by solving the first optimization function. Incidentally, we can recover the speckle noise map through removing the despeckle component directly. Then, we estimate the illumination and reflectance by solving the second optimization function. Further, we apply the contrast-limited adaptive histogram equalization algorithm to adjust the illumination, and project it back to the reflectance for achieving contrast enhancement. Experimental results demonstrate the robustness and effectiveness of our proposed method. It performs well in both speckle reduction and contrast enhancement and is superior to the other two methods both in terms of qualitative analysis and quantitative assessment. Our method has the practical potential to improve the accuracy of manual screening and computer-aided diagnosis for retinal diseases.

Phase unwrapping for Doppler spectral domain optical coherence tomography flow measurement

Doppler optical coherence tomography (OCT) offers additional flow velocity information, which extends the application of OCT. Phase wrapping is the inherent problem that limits measureable range of Doppler OCT. We propose a phase unwrapping method which is suitable for correcting phase in Doppler OCT images. Points (pixels) in flow region are divided into groups according to the radial distance. Points in the same group are supposed to have close velocity. Phase unwrapping algorithm begins at the boundary layer group and is performed sequentially toward the center. Using the proposed criterion, points in a group are separated into two categories, signal points and noise points. Wrapping rounds are determined for signal points phase unwrapping. Mean value of the corrected signal points replaces the noise points for noise reduction. The method is validated with capillary tube flow phantom and in vivo blood flow.

Selective retinex enhancement based on the clustering algorithm and block-matching 3D for optical coherence tomography images

It is important to enhance the contrast and remove the speckle noise for optical coherence tomography (OCT) images. In this paper, we propose a selective retinex enhancement method based on the fuzzy c-means (FCM) clustering algorithm to enhance only the structure part in OCT images and combines with the block-matching 3D (BM3D) algorithm for filtering. In the proposed selective retinex enhancement method, we first calculate the feature image of the original image, which includes the mean value and standard deviation of each pixel in the original image and its correlation image. Second, by applying the FCM clustering algorithm to the feature image, a mask is generated that can distinguish the structure part from the background part in the OCT image. Then, the mask is applied to the multi-scale retinex algorithm, and only the structure part in the OCT image is enhanced. Moreover, the BM3D method is applied to filter the enhanced image. Experimental results demonstrate that the proposed method performs impressively in improving the contrast and removing the speckle noise of OCT images, and it provides better quantitative performance in terms of signal-to-noise ratio, contrast-to-noise ratio, equivalent number of looks, and the edge preservation parameter $ \beta $β.

Robust phase unwrapping for phase images in Fourier domain Doppler optical coherence tomography

To solve the 2 ? phase ambiguity for phase-resolved Doppler images in Doppler optical coherence tomography, we present a modified network programming technique for the first time to the best of our knowledge. The proposed method assumes that error of the discrete derivatives between unwrapped phase image and wrapped phase image can be arbitrary values instead of integer-multiple of 2 ? , which makes the real-phase restoration accurate and robust against noise. We compared our proposed method with the network programming method. Parameters including root-mean-square-error and noise amplification degree were adopted for comparison. The experimental study on simulated images, phantom, and real-vessel OCT images were performed. The proposed method consistently achieves optimal results.