Texture analysis of optical coherence tomography speckle for characterizing biological tissues in vivo

Abstract

We demonstrate a method for differentiating tissue disease states using the intrinsic texture properties of speckle in optical coherence tomography (OCT) images of normal and tumor tissues obtained in vivo. This approach fits a gamma distribution function to the nonlog-compressed OCT image intensities, thus allowing differentiation of normal and tumor tissues in an ME-180 human cervical cancer mouse xenograft model. Quantitative speckle intensity distribution analysis thus shows promise for identifying tissue pathologies, with potential for early cancer detection in vivo.

Generic pixel-wise speckle detection in Fourier-domain optical coherence tomography images

“…By correcting only the speckle areas while leaving the rest unaltered, the speckle noise can be greatly reduced without compromising the image quality. Another application is to differentiate various tissue types based on the speckle patterns, which is similar to the one reported in [13]. As this work proposes a method to directly identify speckles, further quantitative metrics such as speckle density can be obtained.…”

Optical coherence tomography in diagnosing inflammatory diseases of ENT

“…This complicates the interpretation of diagnostic OCT images and requires the development of algorithms for the numerical processing of OCT images to increase diagnostic accuracy of the technique. Such algorithms are usually based on the extraction of numerical parameters of the OCT images, such as individual optical properties of tissue layers [15], parameters of the histograms of entire OCT image [16] or preselected region of interest [17]. Extracted values can be sensitive to morphological alterations in cases when a clinician's visual perception is not able to distinguish changes.…”

“…Numerous studies have consistently proved that the pathological processes in biological tissues significantly change their optical properties, which can be sensed by OCT (see, for example, [15][16][17]). In this study it was possible to show that OCT could be used for diagnosis of inflammatory processes in ENT.…”

Quantitative characterization of mechanically indented in vivo human skin in adults and infants using optical coherence tomography

“…25 Basic statistics, such as the statistical moments and Gamma fitting of the intensity histogram, have also been applied for tissue characterization. [26][27][28] Commonly performed on OCT images, texture analysis often takes advantage of other statistical features that are both intensity sensitive and directionally sensitive, such as the co-occurrence matrices 29 and the gray-scale run-length matrices. 30 In this paper, fractal analysis was utilized to characterize the intensity distribution within the tissue.…”