Biomedical subjects

N S Cosby

Publications and source records attributed to N S Cosby.

3 recordsLinked to original sources

PubMed1998

Computer-aided radiation therapy simulation: image intensifier spatial distortion correction for large field of view digital fluoroscopy.

An accurate method of correcting spatial distortion in digital fluoroscopy images has been developed for generating fluoroscopy-based large field of view images for computer-aided radiation therapy simulation. This method is applicable to arbitrary gantry rotations and arbitrary shifts of the image intensifier relative to the central axis of the x-ray beam. It is therefore suitable for conventional radiation therapy simulation techniques that involve the arbitrary positioning of the image intensifier by the operator. Spatial distortion is modelled as two image intensifier orientation-dependent components, the first resulting from the projection of the x-ray image onto the curved surface of the image intensifier front end, and the second produced by the image intensifier electron optics, interactions with external magnetic fields and the video system. A geometrical model approximates the first component. The second component is modelled by a third-order polynomial transformation. A weighted mean approach is employed to achieve accurate distortion correction when the image intensifier is oriented differently from the calibration orientations. Mean and maximum residual errors (measured in the plane of the isocentre) of 0.4 mm and 1.0 mm respectively have been achieved with just 48 calibration orientations in four dimensions (gantry rotation and lateral, longitudinal and vertical shifts of the image intensifier).

Calibration↗

PubMed1990

An adaptive technique for digital noise suppression in on-line portal imaging.

Two complementary approaches to the noise suppression problem in on-line portal imaging have been analysed. Temporal filtering by image summation can substantially reduce the amount of noise in an image. In many cases, however, movements of the patient or the radiation source limit the time period over which the averaging can be done. Any remaining noise has to be dealt with by applying spatial filtering. The adaptive Lee filter is particularly suitable for portal imaging applications. It preserves a crisp definition of edges while removing noise in flat regions of the image. It can be used to obtain images of satisfactory quality with short radiation exposure of the patient. We have proposed a modification to the basic Lee technique which permits the calculation of the noise variance locally by utilising the information contained in intermediate images acquired during frame averaging. Unlike the original Lee formulation, no a priori knowledge of the noise variance is required, and in contrast to Mastin's approach (Mastin 1985), the variance may vary with position in the image. The tests of performance of the modified Lee filter, carried out using on-line images, have shown its superiority in comparison with the original Lee technique as well as with conventional averaging and median filters.

Filtration↗

PubMedDate not supplied

The enhancement of radiotherapy verification images by an automated edge detection technique.

Adaptive histogram equalization techniques are known to be effective for the enhancement of contrast in portal images acquired during radiotherapy treatments. A significant drawback is the loss of definition on the edges of the treatment field. Analysis of this problem shows that it can be remedied by separating the treatment field from the background prior to the enhancement, and using only the pixels within the field boundary in the enhancement procedure. An edge extraction algorithm has been developed for delineating the treatment field in portal images, and consists of four modules that are applied to the original portal image in sequence. In the first step, edges are enhanced with a derivative of Gaussian operator that assures high response to the field edges relative to anatomical or other edges in the image. Pixels for which the response of the edge operator was the strongest are subsequently connected by an edge following algorithm to produce a raw contour of the field. In the last two steps the contour is refined by converting it into straight line segments and appending to the contour any parts of the field edge that might have been missed out during the initial edge following. The final contour encloses exclusively those pixels that belong to the treatment field, and the adaptive histogram equalization is applied selectively to this region. The combination of edge detection and selective enhancement was shown to produce images of superior contrast on the patient's anatomical features as well as accurate definition of treatment field edges.

Algorithms↗