Reduction In Optical Coherence Tomography Images Research Articles

Reference variance and adjustment factor based wave atoms image speckle reduction method for optical coherence tomography images

Speckle noise in optical coherence tomography (OCT) images causes blurring of small targets and structural edges in the image, which reduces the quality of OCT images. To address this problem, a reference variance and adjustment factor based wave atoms image speckle reduction method for OCT images is proposed. First, reference image and adjustment factor were introduced, and reference image speckle noise reference variance was adjusted using adjustment factor to obtain the target image speckle noise variance. Then, a wave atoms threshold equation including the reference variance and the adjustment factor was constructed to obtain an accurate wave atoms noise reduction threshold. On this basis, speckle noise was shrunk using the wave atoms noise reduction threshold to achieve speckle noise reduction in OCT images, and OCT images with clear small targets and sharp structure edges were obtained. The effectiveness and advantages of the proposed method were illustrated with human eye OCT image and retinal OCT image.

Cluster-based filtering framework for speckle reduction in OCT images.

Optical coherence tomography (OCT) has become a popular modality in the dermatology discipline due to its moderate resolution and penetration depth. OCT images, however, contain a grainy pattern called speckle. To date, a variety of filtering techniques have been introduced to reduce speckle in OCT images. However, further improvement is required to reduce edge smoothing and the deterioration of small structures in OCT images after despeckling. In this manuscript, we present a novel cluster-based speckle reduction framework (CSRF) that consists of a clustering method, followed by a despeckling method. Since edges are borders of two adjacent clusters, the proposed framework leaves the edges intact. Moreover, the multiplicative speckle noise could be modeled as additive noise in each cluster. To evaluate the performance of CSRF and demonstrate its generic nature, a clustering method, namely k-means (KM), and, two pixelwise despeckling algorithms, including Lee filter (LF) and adaptive Wiener filter (AWF), are used. The results indicate that CSRF significantly improves the performance of despeckling algorithms. These improvements are evaluated on healthy human skin images in vivo using two numerical assessment measures including signal-to-noise ratio (SNR), and structural similarity index (SSIM).

Multimodal texture analysis of OCT images as a diagnostic application for skin tumors

Optical coherence tomography (OCT) is an effective tool for determination of pathological topology that reflects structural and textural metamorphoses of tissue. In this paper, we propose a report about our examining of the validity of OCT in identifying changes using a skin cancer texture analysis compiled from Haralick texture features, fractal dimension, complex directional field features and Markov random field method from different tissues. The experimental data set contains 530 OCT images with normal skin and tumors as Basal Cell Carcinoma (BCC), Malignant Melanoma (MM) and Nevus. Speckle reduction is an essential pre-processing part for OCT image analyze. In this work, we used an interval type-II fuzzy anisotropic diffusion algorithm for speckle noise reduction in OCT images (B- and/or C-scans). The Haralick texture features as contrast, correlation, energy, and homogeneity have been calculated in various directions. A box-counting method and other methodologies have been performed to evaluate fractal dimension of skin probes. The complex directional field calculated by the local gradient methodology provides important data for linear dividing of species. We also estimated autocorrelation function using Markov random fields. Additionally, the boosting has been used for the quality enhancing of the diagnosis method. And, finally, artificial neural network (ANN) has been utilized for comparing received rates. Our results demonstrate that these texture features may present helpful information to discriminate a tumor from healthy tissue. We obtained sensitivity about 92% and specificity about 95% for a task of discrimination between MM and healthy skin. Finally, a universal four classes classificatory has been built with average accuracy 75%.

Spiking cortical model-based nonlocal means method for speckle reduction in optical coherence tomography images.

Optical coherence tomography (OCT) images are usually degraded by significant speckle noise, which will strongly hamper their quantitative analysis. However, speckle noise reduction in OCT images is particularly challenging because of the difficulty in differentiating between noise and the information components of the speckle pattern. To address this problem, the spiking cortical model (SCM)-based nonlocal means method is presented. The proposed method explores self-similarities of OCT images based on rotation-invariant features of image patches extracted by SCM and then restores the speckled images by averaging the similar patches. This method can provide sufficient speckle reduction while preserving image details very well due to its effectiveness in finding reliable similar patches under high speckle noise contamination. When applied to the retinal OCT image, this method provides signal-to-noise ratio improvements of >16 dB with a small 5.4% loss of similarity.

Speckle reduction in optical coherence tomography images based on wave atoms.

Optical coherence tomography (OCT) is an emerging noninvasive imaging technique, which is based on low-coherence interferometry. OCT images suffer from speckle noise, which reduces image contrast. A shrinkage filter based on wave atoms transform is proposed for speckle reduction in OCT images. Wave atoms transform is a new multiscale geometric analysis tool that offers sparser expansion and better representation for images containing oscillatory patterns and textures than other traditional transforms, such as wavelet and curvelet transforms. Cycle spinning-based technology is introduced to avoid visual artifacts, such as Gibbs-like phenomenon, and to develop a translation invariant wave atoms denoising scheme. The speckle suppression degree in the denoised images is controlled by an adjustable parameter that determines the threshold in the wave atoms domain. The experimental results show that the proposed method can effectively remove the speckle noise and improve the OCT image quality. The signal-to-noise ratio, contrast-to-noise ratio, average equivalent number of looks, and cross-correlation (XCOR) values are obtained, and the results are also compared with the wavelet and curvelet thresholding techniques.

Denoising during optical coherence tomography of the prostate nerves via wavelet shrinkage using dual-tree complex wavelet transform

The dual-tree complex wavelet transform (CDWT) is a relatively recent enhancement to the discrete wavelet transform (DWT), with important additional properties. It is nearly shift-invariant and directionally selective in two and higher dimensions. In this letter, a locally adaptive denoising algorithm is applied to reduce speckle noise in time-domain optical coherence tomography (OCT) images of the prostate. The algorithm is illustrated using DWT and CDWT. Applying the CDWT provides improved results for speckle noise reduction in OCT images. The cavernous nerve and prostate gland can be separated from discontinuities due to noise, and image quality metrics improvements with a signal-to-noise ratio increase of 14 dB are attained.