Morphological analysis of optical coherence tomography images for automated classification of gastrointestinal tissues

Abstract

The impact of digestive diseases, which include disorders affecting the oropharynx and alimentary canal, ranges from the inconvenience of a transient diarrhoea to dreaded conditions such as pancreatic cancer, which are usually fatal. Currently, the major limitation for the diagnosis of such diseases is sampling error because, even in the cases of rigorous adherence to biopsy protocols, only a tiny fraction of the surface of the involved gastrointestinal tract is sampled. Optical coherence tomography (OCT), which is an interferometric imaging technique for the minimally invasive measurement of biological samples, could decrease sampling error, increase yield, and even eliminate the need for tissue sampling provided that an automated, quick and reproducible tissue classification system is developed. Segmentation and quantification of ophthalmologic pathologies using OCT traditionally rely on the extraction of thickness and size measures from the OCT images, but layers are often not observed in nonopthalmic OCT imaging. Distinct mathematical methods, namely Principal Component Analysis (PCA) and textural analyses including both spatial textural analysis derived from the two-dimensional discrete Fourier transform (DFT) and statistical texture analysis obtained independently from center-symmetric autocorrelation (CSAC) and spatial grey-level dependency matrices (SGLDM), have been previously reported to overcome this problem. We propose an alternative approach consisting of a region segmentation according to the intensity variation along the vertical axis and a pure statistical technique for feature quantification, i.e. morphological analysis. Qualitative and quantitative comparisons with traditional approaches are accomplished in the discrimination of freshly-excised specimens of gastrointestinal tissues to exhibit the feasibility of the proposed method for computer-aided diagnosis (CAD) in the clinical setting.

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.…”

Data Analysis and Signal Postprocessing for Optical Coherence Tomography

“…Texture analysis due to its intuitive simplicity has been widely applied to OCT images. Texture analysis has been used for diagnosis of dysplasia in Barrett's esophagus [96], for automated classification of gastrointenstinal tissues [97], and for differentiating between different human breast tissue types [98]. Texture analysis has also been combined with wavelets to improve classification performance by reducing the impact of system-dependent variations in the speckle pattern [99].…”

Optical coherence tomography assessment of vessel wall degradation in thoracic aortic aneurysms

“…This preliminary region is used to establish the threshold for artifacts detection. The artifacts detection threshold is calculated as Q3-k·IQR, 23,27 with Q3 being the upper quartile and IQR the interquartile range of this region of air, while k is a constant value set to 1.5. Detected artifacts are then interpolated with neighboring pixels to provide more accurate air-sample interface detection.…”

“…On the other hand, textural analysis has been used for image classification 21 and, specifically in OCT applications, it has been used to provide information on the spatial distribution of atherosclerosis plaques 22 or for the detection of gastrointestinal tumor tissue. 23 Once the textural analysis becomes designed, results can be quickly obtained because optimization procedures based on parallelization are feasible. In the present work, disorders on the aortic wall structure become highlighted when the textural approach is implemented.…”