Cluster-based filtering framework for speckle reduction in OCT images

Abstract

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).

Hyperspectral Image Denoising Based on Nonlocal Low-Rank and TV Regularization

“…Our method outperforms the state-of-the-art methods both in visual quality and evaluation criteria. Furthrmore, in further works, we will extend our method to other restoration tasks, such as magnetic resonance imaging (MRI) [41] and optical coherence tomography (OCT) images [42][43][44].…”

Melanoma Biomarkers and Their Potential Application for In Vivo Diagnostic Imaging Modalities

“…Examples of OCT, RCT, and ultrasonography images of melanoma are shown in Figure 9 . It should be noted that many of these imaging modalities are in the investigational phase (see [ 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 ]); i.e., they are not regularly used in clinical practice as of yet, but they could provide promising options in the future when they are better understood and accessible.…”

Biometric Measurement of Anterior Segment: A Review

“…When the scatterers in anterior segment are of the same scale or smaller than the central wavelength of the light source, the interference of several partial waves produces a phenomenon known as speckle [ 183 ]. Methods for speckle reduction are divided into two main categories: Hardware-based methods and software-based methods [ 184 ]. The main hardware-based speckle reduction methods are compounding techniques where multiple decorrelated frames are averaged, such as spatial-compounding, angular-compounding, polarization-compounding, and frequency-compounding [ 183 ].…”

“…Recently, the deconvolution problem further advances by using machine learning algorithms. For instance, a cluster-based speckle reduction framework (CSRF) combining the feature extraction, k-means clustering, median filter, and despeckling was proposed [ 184 ]. Additionally, an expandable learnable despeckling framework to organize the despeckling algorithms in an intelligent manner was presented [ 185 ].…”