Prof. Gerhard de Jager Profile

Prof. Gerhard de Jager

at Univ of Cape Town

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Publications (5)

Proceedings Article | 20 April 2005 Paper

An explanation for the extremely low, but variable, radiation dosages measured in a linear slit scanning radiography system

J. Potgieter, Mattieu de Villiers, Martin Scheelke, Gerhard de Jager

Proceedings Volume 5745, (2005) https://doi.org/10.1117/12.595216

KEYWORDS: Skin, Radiography, Sensors, X-rays, Diagnostics, X-ray detectors, Radiology, Chest, X-ray sources, Chest imaging

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Proceedings Article | 5 June 2003 Paper

Detective quantum efficiency of the LODOX system

Mattieu de Villiers, Gerhard de Jager

Proceedings Volume 5030, (2003) https://doi.org/10.1117/12.479967

KEYWORDS: Modulation transfer functions, Sensors, X-rays, Quantum efficiency, Imaging systems, Photons, Image resolution, Digital x-ray imaging, Digital imaging, Manufacturing

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Proceedings Article | 13 September 1995 Paper

Real-time machine vision inspection of plastic bottle closures

Trevor Bartleet, Gerhard de Jager

Proceedings Volume 2598, (1995) https://doi.org/10.1117/12.220917

KEYWORDS: Digital signal processing, Image processing, Inspection, Detection and tracking algorithms, Machine vision, Computing systems, Imaging systems, Cameras, Light sources and illumination, Frame grabbers

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Proceedings Article | 11 May 1994 Paper

Automatic registration of temporal image pairs for digital subtraction angiography

Greg Cox, Gerhard de Jager

Proceedings Volume 2167, (1994) https://doi.org/10.1117/12.175053

KEYWORDS: Image registration, Blood vessels, Image enhancement, X-rays, Image quality, Digital imaging, X-ray imaging, Angiography, Image contrast enhancement, Image processing

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Temporal Digital Subtraction Angiography (DSA) is used to visualize blood vessels in x-ray images. A DSA image pair consists of the mask image, which is a digitized x-ray taken before a contrast medium is injected into the bloodstream, and the live image, which is taken once the contrast medium has traversed the circulatory system and reached the blood vessels of interest. The mask image is then subtracted from the live image and ideally only the contrast enhanced blood vessels should remain. DSA has two main limitations. Firstly, gross patient motion and physiological events occur in the time that elapses between x-rays. Secondly, there are local and global differences in the mean gray-level at corresponding points in the live and mask images, excluding the variations introduced by the contrast media. To solve the motion problem, we take the approach of matching regions around control points in the live image in a search area around the approximately corresponding points in the mask image. In this way a motion vector field that describes the spatial offset to the best match position in the mask image (with subpixel accuracy) is constructed. The problem of mean gray- level disparity between the live and mask images is to a large extent overcome by the use of a match measure that is invariant to overall additive gray-level differences. Incorrect mismatches caused by the contrast media are avoided by using multiple subtemplates in the matching process. The subtemplate method also allows the estimation of mean gray-level disparity between the mask and live images. The smoothed motion vector field and mean gray-level disparity estimates are used to perform an improved subtraction of the mask from the live image with a reduction in the artifacts that are a result of normal subtraction. Efficient best match search techniques are used to reduce the computational cost of the algorithm, at the expense of some difference image quality. Results are provided for simulated and actual DSA image pairs.

Proceedings Article | 23 March 1994 Paper

Model-based segmentation of medical x-ray images

Frederick Hoare, Gerhard de Jager

Proceedings Volume 2182, (1994) https://doi.org/10.1117/12.171085

KEYWORDS: X-rays, Image segmentation, X-ray imaging, Image processing, Chest, Lung, Video processing, Image enhancement, Modeling, Edge detection

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