University of Pittsburgh

Chaim-Gadi Wollstein

Joel Steven Schuman

Hiroshi Ishikawa

Chieh-Li Chen

Juan Xu

The present invention is directed to systems and methods for generating virtually averaged optical coherence tomography (Oct.) images. An illustrative method can include receiving an image, identifying a first voxel of the image, and selecting a plurality of local voxels of the image. Each of the plurality of local voxels is within a defined region of the first voxel. The values of the plurality of local voxels can indicate an appearance of the local voxels in the image.

Image enhancement using virtual averaging

The present disclosure is directed to systems and methods for normalizing an image of an eye among multiple optical coherence tomography (OCT) devices. The system can receive an OCT image and generate a normalized OCT image. The system is configured to normalize a sample density of the received image, normalize an amount of noise using a suitable noise reduction technique, normalize a distribution of signal amplitudes of the image, and then normalize a signal quality of the image. The resulting normalized image of the eye can then be used in the comparison among OCT images taken from multiple OCT devices.

Signal normalization among multiple optical coherence tomography devices

A fully automated optic nerve head assessment system, based on spectral domain optical coherence tomography, provides essential disc parameters for clinical analysis, early detection, and monitoring of progression.

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Pittsburgh, PA, United States of America

Chaim-Gadi Wollstein