Showing papers on "Standard test image published in 1987"

Image mosaic-processing method and apparatus

Abstract: As image processing method suitable for creative image generation in which the image data of an original image are divided into plural areas each consisting of plural pixels, the shape of these areas is modified according to the image data of the original image, and the pixel data in each area are processed in accordance with a representative value of the image data.

Auto-Ranging And Normalization Versus Histogram Modifications For Automatic Image Processing Of Digital Radiographs

Abstract: Detectors of computed radiography systems have a very wide linear dynamic range Pnd enable faithful acquisition of image data, independent of the chosen x-ray exposure or the dynamic data range within the image. To yield consistently viewable images of satisfactory quality without direct user interaction with the image, semi-automatic scaling of the clinically useful data span to the available display range must be achieved. Three methods are discussed: Detector auto-ranging and renormalization of the filtered image, local histogram equalization, and anatomy specific auto-ranging of the filtered image. Presently, the first method is favored and employed clinically.

Laplacian pyramid image data compression

Abstract: With the growing demand for image processing, efficient image data compression techniques are much needed for image data transmission and storage. Two image data compression techniques based on the Laplacian Pyramids are examined: the Laplacian Pyramid Vector Quantization (LPVQ) and the Laplacian Pyramid Predictive Compression (LPPC). The LPVQ can provide a reduction from 8 bits per pixel (bpp) to 0.45 bpp with negligible degradation in image quality (0.6% rms reconstruction error), as shown in a test image. The LPPC can provide comparable performance with a proper choice of image model. Both techniques thus can be superior to existing transform coding and predictive compression techniques. The problems of additive noise, pre-and post-processing and hardware realization are also considered.

Object Identification And Measurement From Images With Access To The Database To Select Specific Subpopulations Of Special Interest

Abstract: An automated vision image analysis system to analyze images resulting from nucleic acid hybridizations is presented. Real-time image acquisition from autoradiographs is accomplished with a sensitivity to optical density to the degree that any spot visible by eye can be quantified by digital methods and enhanced by image processing techniques for observation. The system provides a tool for quantification of differences in gene expression, rapid processing of thousands of pairs of genes, storage in a database, search for small differences, large differences, and infrequently occuring genes. Background correction, normalization and pseudocolor "mapping" facilitate comparison and quantification directly from the CRT display. The image pairs, a percent change image and a difference image are presented in a quad display with coordinated spot density and change measurement simultaneously for each of the four images. The system provides a tool for analysis of altered gene expression in response to environmental stress, such as heat-shock and salt water in plant growth; hormone regulation, such as before and after hormone administration in animals; development; and carcinogenesis. The use of this system to study nucleic acid hybridization is contrasted with the study of 2-D protein electrophoresis gels for the determination of changes in gene expression and identification of corresponding proteins of significance.

Convolution Squared Error Versus Observer Preference

Abstract: An automatically computable fidelity measure which correlates well with observer preference is needed to facilitate the optimization of image processing methods. This study evaluates the use of the convolution mean squared error (CMSE) as such a measure. To compute the CMSE, both the true image and the "test image" are passed through a filter or other processing system. The mean squared error between the two identically processed images is then determined. A high-pass filter and, separately, a low-pass filter are optimized for this purpose. The inclusion of an early visual system model before these filters is also evaluated. The true image used was a high-resolution, high-count, nuclear medicine image of a liver and spleen phantom. Simulated acquisitions of this true image, which had been restored using the constrained least squares method with one of nine coarseness functions, provided the "test images." A low-pass filter of low cut-off frequency and low order gave CMSE values which correlated well (Spearman rank correlation coefficient (rs) of 0.88) with average ranks from observer preference studies. A high-pass filter of high order and high cut-off frequency yielded similar results (rs = 0.86).© (1987) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Arrangement for forming cross correlation coefficients

Abstract: The arrangement for forming cross correlation coefficients between a pattern image and a test image is used for determining whether a predetermined pattern exists in a test image. For this purpose, the correlation coefficient must be determined at various positions of the two images with respect to one another which requires a large amount of computation. To carry out the calculation of each correlation coefficient in parallel with the simplest possible analog means, the test image signals are converted into difference signals of successive pixels, and these signals are progressively non-linearly compressed. As a result, the data to be processed have only a small dynamic range and can be processed by simple analog elements. Furthermore, a simple circuit for determining the complete cross correlation coefficient is specified.