The main steps of image analysis are image capturing, image storage (compression), correcting imaging defects (e.g. non-uniform illumination, electronic-noise, glare effect), image enhancement, segmentation of objects in the image and image measurements. Digitisation is made by a camera. The most modern types include a frame-grabber, converting the analog-to-digital signal into digital (numerical) information. The numerical information consists of the grey values describing the brightness of every point within the image, named a pixel. The information is stored in bits. Eight bits are summarised in one byte. Therefore, grey values can have a value between 0 and 256 (2(8)). The human eye seems to be quite content with a display of 5-bit images (corresponding to 64 different grey values). In a digitised image, the pixel grey values can vary within regions that are uniform in the original scene: the image is noisy. The noise is mainly manifested in the background of the image. For an optimal discrimination between different objects or features in an image, uniformity of illumination in the whole image is required. These defects can be minimised by shading correction [subtraction of a background (white) image from the original image, pixel per pixel, or division of the original image by the background image]. The brightness of an image represented by its grey values can be analysed for every single pixel or for a group of pixels. The most frequently used pixel-based image descriptors are optical density, integrated optical density, the histogram of the grey values, mean grey value and entropy. The distribution of the grey values existing within an image is one of the most important characteristics of the image. However, the histogram gives no information about the texture of the image. The simplest way to improve the contrast of an image is to expand the brightness scale by spreading the histogram out to the full available range. Rules for transforming the grey value histogram of an existing image (input image) into a new grey value histogram (output image) are most quickly handled by a look-up table (LUT). The histogram of an image can be influenced by gain, offset and gamma of the camera. Gain defines the voltage range, offset defines the reference voltage and gamma the slope of the regression line between the light intensity and the voltage of the camera. A very important descriptor of neighbourhood relations in an image is the co-occurrence matrix. The distance between the pixels (original pixel and its neighbouring pixel) can influence the various parameters calculated from the co-occurrence matrix. The main goals of image enhancement are elimination of surface roughness in an image (smoothing), correction of defects (e.g. noise), extraction of edges, identification of points, strengthening texture elements and improving contrast. In enhancement, two types of operations can be distinguished: pixel-based (point operations) and neighbourhood-based (matrix operations). The most important pixel-based operations are ...

Photon correlation studies in H2O/sodium di‐2‐ethylhexylsulfosuccinate (AOT)/i‐C8H18‐systems, pertinent IR. investigations, and vapor pressure osmometric measurements with alkylated quaternary ammonium di‐2‐ethylhexylsulfosuccinates strongly suggest water to be pre‐requistite for the micellization in apolar media.

acetylene in the Gas Phase and in Solution in the Range 1100 to 4000Summary. The electronic spectra of the title compounds I(n), n = 1 to 5, were recorded under standard conditions for quantitative comparison. Spectra of 1(1) to 1(4) in the gas phase and of I(2) to 1(5) in nonpolar solutions are presented in a computer plotted form, and wave length maxima and intensities are listed. Tcntative assignments of the medium-intensity, first transition (A band) and the ultrahigh-intensity, second transition (B band) are given. Finally, spectra of I(2) to 1(5) rccorded at -150" are presented and discussed (A band). The syntheses of 1(3) to 1(5) are given in detail. treatment will appear in a separate paper [12].

Telepathology may be used to provide a frozen section service to hospitals without a department or institute of pathology. We have developed a telepathology system using the commercially available Integrated Services Digital Network (ISDN). The main software and hardware elements of our system are: Apple Macintosh workstations, a program for simultaneous transfer of image, voice and data, and a data bank for storage of patients' data and microscopic images. A picture instrument manager (PIM) makes remote control of microscopes or other instruments possible. The system connects the Department of Pathology of the University of Basel with the Regional Hospital of Samedan, 250 km away, and the Regional Hospital of Burgdorf, 100 km away. During a period of 20 months, frozen sections with the hospitals in Samedan and Burgdorf were performed in 53 patients. Between 54 and 58 s were required for the transfer of a diagnostic 8-bit grey level image containing 341 +/- 26.1 (standard error) kbytes (n = 13) or a diagnostic 24-bit colour image containing 165 +/- 16.9 kbytes (n = 40). Frozen section diagnosis was completed in 20-40 min. True-positive diagnoses of malignant tumours were achieved in 85.7% of cases (sensitivity = 0.857). No false-positive diagnosis was made. In 3 of the 53 cases telepathological diagnosis was not possible for technical reasons.

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