Optical coherence tomography (OCT) images are affected by artefacts. These artefacts are the result of different factors such as refraction, curvature of the intermediate layers up to the depth of interest and the scanning procedure. The effect of such errors is different, depending on the way the image is acquired, either en-face or longitudinal OCT. We quantify the distortions by evaluating a lateral and an axial error. These measure the lateral and axial deviations of each image point from the object point inside the tissue. We show that the axial distortion can be larger than the achievable depth resolution in modern OCT systems. We have investigated these errors in imaging different tissue: cornea and retina in vivo and an intraocular lens in vitro.

Optical coherence tomography (OCT) is a well-established imaging method in the ophthalmic practice. We describe a novel corneal topography method that directly measures anterior cornea surface elevation from a single en face OCT image. This method uses an OCT interferometer configuration equipped with a multiple delay element (MDE) in the reference arm. The MDE selects multiple axial positions within the target object, simultaneously, which leads to information from multiple axial distances to be cumulated in a single en face OCT frame. When an en face OCT scan of a cornea is acquired with such an OCT setup, the resulting image contains nonoverlapping circular contours. Images of a reflective metallic sphere obtained using this method are used to numerically calibrate the setup. Using these calibration results, position information contained in the en face images from the cornea can be measured directly obtaining three-dimensional coordinates for multiple points located on the cornea surface. From these points, the topographic map of the anterior cornea surface can be generated, using interpolation or Zernike polynomial decomposition. Experimental results of in vivo cornea topography obtained with this method are presented.

In this paper a framework is presented for extracting information content from modern sky surveys, which have archived multiple terabytes of data in various wavelengths and at various resolutions. The proposed framework includes new technology that addresses the massive size and geographically distributed nature of these data sets. Also included is automated support for combining data sets from multiple archives and for relating sky catalogs to the image data. In addition, tools are provided for efficiently exploring images that are hundreds of gigabytes or even multiple terabytes in size. The proposed framework and "data agile" applications described here are essential in the modern era of astronomy because images of this size far exceed the current capabilities of conventional image analysis tools used in the astronomical community.

ABSTRACT:Cloud based image storage and processing requires revaluation of formats and processing methods. For the true value of the massive volumes of earth observation data to be realized, the image data needs to be accessible from the cloud. Traditional file formats such as TIF and NITF were developed in the hay day of the desktop and assumed fast low latency file access. Other formats such as JPEG2000 provide for streaming protocols for pixel data, but still require a server to have file access. These concepts no longer truly hold in cloud based elastic storage and computation environments. This paper will provide details of a newly evolving image storage format (MRF) and compression that is optimized for cloud environments. Although the cost of storage continues to fall for large data volumes, there is still significant value in compression. For imagery data to be used in analysis and exploit the extended dynamic range of the new sensors, lossless or controlled lossy compression is of high value. Compression decreases the data volumes stored and reduces the data transferred, but the reduced data size must be balanced with the CPU required to decompress. The paper also outlines a new compression algorithm (LERC) for imagery and elevation data that optimizes this balance. Advantages of the compression include its simple to implement algorithm that enables it to be efficiently accessed using JavaScript. Combing this new cloud based image storage format and compression will help resolve some of the challenges of big image data on the internet.

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