Endoscopic Optical Coherence Tomography (EOCT) systems can perform in vivo, real-time, high-resolution imaging of the human esophagus and, thus, play an important role in the earlier diagnosis and better prognosis of esophageal diseases such as Barrett’s, dysplasia and adenocarcinoma. However, the high image throughput and massive data volumes make manual evaluation of the generated information extremely difficult. Unfortunately, the algorithms, developed thus far, have not been able to provide effective computer-aided diagnosis. In this study, we compare different machine learning methods for tissue segmentation and classification of esophageal tissue in in vivo OCT images. An automated algorithm was developed, capable of discriminating normal tissue from Barrett’s Esophagus (BE) and dysplasia. The classification was based on various features of the epithelium, extracted from EOCT images, such as intensity-based statistics, the group velocity dispersion (GVD), estimated from the image speckle, and the scatterer size, calculated using the bandwidth of the correlation of the derivative (COD) method. The comparison and evaluation of various machine learning techniques has shown that a neural network based approach provided the best performance, classifying Barret’s esophagus and dysplasia, for individual A-Scans, with an accuracy of 89%.
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