Texture analysis of optical coherence tomography images: feasibility for tissue classification

Abstract

Optical coherence tomography (OCT) acquires cross-sectional images of tissue by measuring back-reflected light. Images from in vivo OCT systems typically have a resolution of 10 to 15 mm, and are thus best suited for visualizing structures in the range of tens to hundreds of microns, such as tissue layers or glands. Many normal and abnormal tissues lack visible structures in this size range, so it may appear that OCT is unsuitable for identification of these tissues. However, examination of structure-poor OCT images reveals that they frequently display a characteristic texture that is due to speckle. We evaluated the application of statistical and spectral texture analysis techniques for differentiating tissue types based on the structural and speckle content in OCT images. Excellent correct classification rates were obtained when images had slight visual differences (mouse skin and fat, correct classification rates of 98.5 and 97.3%, respectively), and reasonable rates were obtained with nearly identical-appearing images (normal versus abnormal mouse lung, correct classification rates of 64.0 and 88.6%, respectively). This study shows that texture analysis of OCT images may be capable of differentiating tissue types without reliance on visible structures.

Using optical coherence tomography to evaluate skin sun damage and precancer

“…OCT has been shown to be useful in non-invasive monitoring of cutaneous inflammation [6], hyperkeratotic conditions, and photoadaptive processes [7][8][9]. While diagnosis based on OCT images is normally made on the basis of image features, texture analysis of OCT images has also been used to classify the tissue type [10]. OCT has shown promise as a diagnostic tool for the detection of skin cancer.…”

“…We are currently conducting a longitudinal study to test this hypothesis. Future work will also include using texture analysis to evaluate OCT images, as we have shown in previous work that texture analysis can distinguish between tissue types even in the absence of visible OCT image features [10]. Flow chart showing the major steps in the study, including numbers of patients, images, and biopsies obtained, types of statistical image analysis, images excluded, and analysis results.…”

Texture Analysis on Image Motif of Endek Bali using K-Nearest Neighbor Classification Method

“…Terms formation of a texture is: Humans look at the texture based on the description that is random, such as smooth, rough, regular, irregular, and soon. This is a description of improper and non-quantitative, so it needed a quantitative description (mathematical) to facilitate analysis [6][7][8].…”

“…Texture analysis has also been used to evaluate ultrasound images of the prostate. Other optical imaging modalities have utilized texture analysis, such as fluorescence microscopy images of colonic tissue sections and light microscopy images of the chromatin structure in advanced prostate cancer [6].…”

Optical coherence tomography—current technology and applications in clinical and biomedical research

“…The speckle pattern, appearing as a grainy fine structure in OCT images, is caused by interference of light reflected from many randomly distributed scatterers within the probing volume [54]. Speckle characteristics, such as the size and the intensity distribution can provide additional information about the underlying scatterers [55,56]. However, the speckle texture obscures small features in the image and is usually considered a source of noise.…”