Adaptive histogram equalization and its variations
Computer Vision, Graphics, and Image Processing
Contrast limited adaptive histogram equalization
Graphics gems IV
Digital Image Processing
Gradient domain high dynamic range compression
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Fast bilateral filtering for the display of high-dynamic-range images
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Photographic tone reproduction for digital images
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
A tone mapping algorithm for high contrast images
EGRW '02 Proceedings of the 13th Eurographics workshop on Rendering
Adaptive gain control for high dynamic range image display
SCCG '02 Proceedings of the 18th spring conference on Computer graphics
Edge-Affected Context for Adaptive Contrast Enhancement
IPMI '91 Proceedings of the 12th International Conference on Information Processing in Medical Imaging
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Computerized Tomography (CT) images are High Dynamic Range (HDR) images of the X-ray attenuation coefficients of the body's tissues. The inability to see abnormalities in tissues with marked differences in their X-ray attenuation coefficients, in a single CT window, poses a significant clinical problem in radiology. In order to provide proper contrast, which reveals all the required clinical details within each specifically imaged tissue, a single CT slice must be viewed by a radiologist four times: the first viewing focuses on the lung window; the second viewing focuses on the soft tissues window; the third viewing focuses on the liver window; and the fourth viewing focuses on the bone window. In order to enhance the ability to perform a complete diagnosis, while decreasing diagnostic time, we developed the BACCT (Biologically-based Algorithm for Companding CT images) method. Our algorithm compresses and expands (compands) the HDR CT image into a single, low dynamic range image. Before performing the companding procedure, unique processing is required which involves operations that enhance and stretch the image. The performance of our algorithm has been demonstrated on a large repertoire of CT body images. All the clinically required CT information is exposed in each CT slice in a single image. The algorithm compands the CT images in a fully automatic way. Collaborating radiologists have already tested the results of our algorithmic method, and reported that the images seem to provide all the necessary information. However, clinical tests for statistical reliability are still required.