Accurate and automated image segmentation of 3D optical coherence tomography data suffering from low signal-to-noise levels.

Optical coherence tomography (OCT) has proven to be a useful tool for investigating internal structures in ceramic tapes, and the technique is expected to be important for roll-to-roll manufacturing. However, because of high scattering in ceramic materials, noise and speckles deteriorate the image quality, which makes automated quantitative measurements of internal interfaces difficult. To overcome this difficulty we present in this paper an innovative image analysis approach based on volumetric OCT data. The engine in the analysis is a 3D image processing and analysis algorithm. It is dedicated to boundary segmentation and dimensional measurement in volumetric OCT images, and offers high accuracy, efficiency, robustness, subpixel resolution, and a fully automated operation. The method relies on the correlation property of a physical interface and effectively eliminates pixels caused by noise and speckles. The remaining pixels being stored are the ones confirmed to be related to the target interfaces. Segmentation of tilted and curved internal interfaces separated by ∼10  μm in the Z direction is demonstrated. The algorithm also extracts full-field top-view intensity maps of the target interfaces for high-accuracy measurements in the X and Y directions. The methodology developed here may also be adopted in other similar 3D imaging and measurement technologies, e.g., ultrasound imaging, and for various materials.