John D. Austin

Fast spheres, shadows, textures, transparencies, and imgage enhancements in pixel-planes

Pixel-planes is a logic-enhanced memory system for raster graphics and imaging. Although each pixel-memory is enhanced with a one-bit ALU, the systems real power comes from a tree of one-bit adders that can evaluate linear expressions Ax+By+C for every pixel (x,y) simultaneously, as fast as the ALUs and the memory circuits can accept the results. We and others have begun to develop a variety of algorithms that exploit this fast linear expression evaluation capability. In this paper we report some of those results. Illustrated in this paper is a sample image from a small working prototype of the Pixel-planes hardware and a variety of images from simulations of a full-scale system. Timing estimates indicate that 30,000 smooth shaded triangles can be generated per second, or 21,000 smooth-shaded and shadowed triangles can be generated per second, or over 25,000 shaded spheres can be generated per second. Image-enhancement by adaptive histogram equalization can be performed within 4 seconds on a 512x512 image.

Adaptive Histogram Equalization For Automatic Contrast Enhancement Of Medical Images

With the large number of images that will be viewed simultaneously in a medical picture archiving and communication system (PACS) system in the diagnosis of a particular patient, image by image interactive contrast enhancement, at present by intensity windowing, becomes unacceptably time-consuming. Furthermore, windowing has disadvantages of being non-reproducible and providing adequate contrast primarily in selected image regions. The method of adaptive histogram equalization (ahe) appears to provide a solution to these problems. It is reproducible, automatic, and simultaneously provides contrast in all image regions. After summarizing the basic method, this paper will 1) describe a new contrast limited form of ahe that appears to allow its application to a wide variety of medical images, 2) present a VLSI machine design that will allow the calculation of ahe in a fraction of a second per megapixel, and 3) report the results of a study demonstrating that for chest CT images, ahe provides no measurable loss of diagnostic performance compared to the now standard windowing.

MAHEM: a multiprocessor engine for fast contrast-limited adaptive histogram equalization

variant of AHE, theresult value of a pixel is computed as a weighted average of itsinitial value and its rank within its contextual region; the amount ofcontrast in that area of the image determines the weighting. CLAHEhas been found useful in enhancing images from a variety of imagingmodalities, including Computed Tomography (CT; see Figure 1), MagneticResonance Imaging (MRI) ,

A Multiprocessor Adaptive Histogram Equalization Machine

Contrast Limited Adaptive Histogram Equalization (clahe) provides excellent contrast enhancement of medical images, but may be too slow for regular use in a clinical setting. The essential properties of real clahe and artifacts that may be present in an interpolated clahe algorithm are discussed. An alternate form of the clahe algorithm that can be computed quickly on special purpose parallel hardware is described, as well as the architecture for such a machine.

Algorithms For Adaptive Histogram Equalization

Adaptive histogram equalization (ahe) is a contrast enhancement method designed to be broadly applicable and having demonstrated effectiveness [Zimmerman, 1985]. However, slow speed and the overenhancement of noise it produces in relatively homogeneous regions are two problems. We summarize algorithms designed to overcome these and other concerns. These algorithms include interpolated ahe, to speed up the method on general purpose computers; a version of interpolated ahe designed to run in a few seconds on feedback processors; a version of full ahe designed to run in under one second on custom VLSI hardware; and clipped ahe, designed to overcome the problem of overenhancement of noise contrast. We conclude that clipped ahe should become a method of choice in medical imaging and probably also in other areas of digital imaging, and that clipped ahe can be made adequately fast to be routinely applied in the normal display sequence.

Interactive 3D Display of Medical Images

3D display has the potential for increasing the information from medical images that can be used for diagnosis or treatment. It can be accomplished both by displays which present reflections from computed surfaces and by translucent, projective displays. Taking into account both types of display, this paper will (1) discuss the strengths and weaknesses of 3D display and indicate areas of medical imaging where it seems that these strengths can be capitalized upon. (2) survey the kinds of preprocessing necessary to prepare an image for 3D display from a series of 2D grey-scale slice images. (3) present types of interaction and display features that are important as part of 3D display, in particular for projective, translucent 3D display. (4) summarize the features of system software at UNC that supports the required interaction and display on a particular projective 3D display, a varifocal mirror system designed as an add-on to a color raster graphics system [Fuchs, Pizer, et al, 1982a,b].