Towards automated classification of clinical optical coherence tomography data of dense tissues
The native contrast of optical coherence tomography (OCT) data in dense tissues can pose a challenge for clinical decision making. Automated data evaluation is one way of enhancing the clinical utility of measurements. Methods for extracting information
from structural OCT data are appraised here. A-scan analysis allows characterization of layer thickness and scattering parameters,
whereas image analysis renders itself to segmentation, texture and speckle analysis. All fully automated approaches combine
pre-processing, feature registration, data reduction, and classification. Pre-processing requires de-noising, feature recognition,
normalization and refining. In the current literature, image exclusion criteria, initial parameters, or manual input are common
requirements ...
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