Automatic x-ray image contrast enhancement based on parameter auto-optimization

Purpose

Insufficient image contrast associated with radiation therapy daily setup x‐ray images could negatively affect accurate patient treatment setup. We developed a method to perform automatic and user‐independent contrast enhancement on 2D kilo voltage (kV) and megavoltage (MV) x‐ray images. The goal was to provide tissue contrast optimized for each treatment site in order to support accurate patient daily treatment setup and the subsequent offline review.

Methods

The proposed method processes the 2D x‐ray images with an optimized image processing filter chain, which consists of a noise reduction filter and a high‐pass filter followed by a contrast limited adaptive histogram equalization (CLAHE) filter. The most important innovation is to optimize the image processing parameters automatically to determine the required image contrast settings per disease site and imaging modality. Three major parameters controlling the image processing chain, i.e., the Gaussian smoothing weighting factor for the high‐pass filter, the block size, and the clip limiting parameter for the CLAHE filter, were determined automatically using an interior‐point constrained optimization algorithm.

Results

Fifty‐two kV and MV x‐ray images were included in this study. The results were manually evaluated and ranked with scores from 1 (worst, unacceptable) to 5 (significantly better than adequate and visually praise worthy) by physicians and physicists. The average scores for the images processed by the proposed method, the CLAHE, and the best window‐level adjustment were 3.92, 2.83, and 2.27, respectively. The percentage of the processed images received a score of 5 were 48, 29, and 18%, respectively.

Conclusion

The proposed method is able to outperform the standard image contrast adjustment procedures that are currently used in the commercial clinical systems. When the proposed method is implemented in the clinical systems as an automatic image processing filter, it could be useful for allowing quicker and potentially more accurate treatment setup and facilitating the subsequent offline review and verification.

Fast computation of Hessian-based enhancement filters for medical images

This paper presents a method for fast computation of Hessian-based enhancement filters, whose conditions for identifying particular structures in medical images are associated only with the signs of Hessian eigenvalues. The computational costs of Hessian-based enhancement filters come mainly from the computation of Hessian eigenvalues corresponding to image elements to obtain filter responses, because computing eigenvalues of a matrix requires substantial computational effort. High computational cost has become a challenge in the application of Hessian-based enhancement filters. Using a property of the characteristic polynomial coefficients of a matrix and the well-known Routh-Hurwitz criterion in control engineering, it is shown that under certain conditions, the response of a Hessian-based enhancement filter to an image element can be obtained without having to compute Hessian eigenvalues. The computational cost can thus be reduced. Experimental results on several medical images show that the method proposed in this paper can reduce significantly the number of computations of Hessian eigenvalues and the processing times of images. The percentage reductions of the number of computations of Hessian eigenvalues for enhancing blob- and tubular-like structures in two-dimensional images are approximately 90% and 65%, respectively. For enhancing blob-, tubular-, and plane-like structures in three-dimensional images, the reductions are approximately 97%, 75%, and 12%, respectively. For the processing times, the percentage reductions for enhancing blob- and tubular-like structures in two-dimensional images are approximately 31% and 7.5%, respectively. The reductions for enhancing blob-, tubular-, and plane-like structures in three-dimensional images are approximately 68%, 55%, and 3%, respectively.

AAPM/RSNA physics tutorial for residents: physics of flat-panel fluoroscopy systems: Survey of modern fluoroscopy imaging: flat-panel detectors versus image intensifiers and more

This article reviews the design and operation of both flat-panel detector (FPD) and image intensifier fluoroscopy systems. The different components of each imaging chain and their functions are explained and compared. FPD systems have multiple advantages such as a smaller size, extended dynamic range, no spatial distortion, and greater stability. However, FPD systems typically have the same spatial resolution for all fields of view (FOVs) and are prone to ghosting. Image intensifier systems have better spatial resolution with the use of smaller FOVs (magnification modes) and tend to be less expensive. However, the spatial resolution of image intensifier systems is limited by the television system to which they are coupled. Moreover, image intensifier systems are degraded by glare, vignetting, spatial distortions, and defocusing effects. FPD systems do not have these problems. Some recent innovations to fluoroscopy systems include automated filtration, pulsed fluoroscopy, automatic positioning, dose-area product meters, and improved automatic dose rate control programs. Operator-selectable features may affect both the patient radiation dose and image quality; these selectable features include dose level setting, the FOV employed, fluoroscopic pulse rates, geometric factors, display software settings, and methods to reduce the imaging time.