Abstrakt Massmedierna har blivit en allt större del av vårt historieskapande och är med och skapar vår världsbild. Vissa pressbilder når närmast ikonstatus och blir våra referenspunkter när vi reflekterar över händelsen. Därför har jag valt att titta närmare på de bilder som har publicerats i den amerikanska bildtidningen Life, den svenska bildtidningen Se och de svenska kvällstidningarna Expressen och Aftonbladet under Tet-offensiven i februari 1968. I min undersökning har jag samlat in alla bilder från de aktuella tidningarna under perioden och kategoriserat dem efter grupperna militära, militära/civila och civila. Bilderna i kategorin militär var så många att jag valde att dela upp dem i underkategorierna strid, fångar, rutin och döda/sårade. Därefter har jag tittat närmare på bilderna för att få fram hur de olika parterna i kriget framställdes och vilka likheter och skillnader som fanns i materialet i de olika tidningarna. Därefter genomförde jag bildanalyser på en fyra slumpvist utvald bilder, en från varje underkategori i avdelningen militära. Min undersökning visar att tidningarna har haft tillgång till i stort sett samma bildmaterial men att vinklingen i reportagen skiljer en del beroende på vilken sorts bilder de väljer att fokusera på. Identifieringen skedde med de amerikanska trupperna och de vietnamesiska parterna utkristalliserades som de andra. Min undersökning visar att tidningarna har följande fokus. • Life Stort fokus på militära bilder med motiven döda/sårade och rutin. • Se Klart jämnast fördelning över de tre huvudkategorierna. Störst fokus på fångar och de civila. • Expressen Fokus på militära bilder med motiven rutin och döda/sårade. Har även en stor andel bilder som berör de civila. • Aftonbladet Stort fokus på militära bilder med motiven rutin, döda/sårade och strid.

Diss. Linköping : Univ., 2002.

Diss. (sammanfattning) Stockholm : Stockholms universitet, 2005. / Härtill 6 uppsatser.

Licentiatavhandling Linköping : Linköpings universitet, 2008.

Licentiatavhandling Linköping : Linköpings universitet, 2008.

<p><b>Objective:</b></p><p>To develop and validate computer-assisted morphometrical methods, based on stereological theory, in order to facilitate the analysis and quantitative measurements of biological tissue components.</p><p><b>Material and methods:</b></p><p>Biopsy specimens from the vaginal wall or from the vestibulum vaginae of healthy women, or from women suffering from incontinence or vestibulitis were used.</p><p>A number of histochemical methods for light microscopy were used, and modified for the different morphometrical analyses. Electron microscopy was used to reveal collagen fibre diameter.</p><p>Computer-assisted morphometry, based on image analysis and stereology, was employed to analyse the different tissue components in the biopsies. Computer programs for these purposes were developed and validated.</p><p><b>Results:</b></p><p>The results show that computer-assisted morphometry is of great value for quantitative measurements of the following tissue components:</p><p><b>Epithelium:</b> The epithelial structure, instead of just thickness, was measured in an unbiased way.</p><p><b>Collagen:</b> The collagen fibril diameter was determined in electron microscopic specimens, and the collagen content was analysed in light microscopic specimens.</p><p><b>Elastic fibres:</b> The amount of elastic fibres in the connective tissue was measured after visualisation by autofluorescence.</p><p><b>Vasculature:</b> A stereological method using a cycloid grid was implemented in a computer program. Healthy subjects were compared with patients suffering from vestibulitis. The results were identical in the two groups.</p><p><b>Smooth muscle:</b> A stereological method using a point grid was implemented in a computer program. Using the Delesse principle, the fibres were calculated as area fractions. The area fractions were highly variable among the different specimens.</p><p><b>Conclusion:</b></p><p>Morphometry, used correctly, is an important analysis method in histopathological research. It is important that the methods are as simple and user-friendly as possible. The present studies show that this methodology can be applied for most quantitative histological analyses.</p>

<p>For marine sciences sometimes there is a need to perform underwater photography. Optical properties of light cause severe quality problems for underwater photography. Light of different energies is absorbed at highly different rates under water causing significant bluishness of the images. If the colour dependent attenuation under water can be properly estimated it should be possible to use computerised image processing to colour correct digital images using Beer’s Law.</p><p>In this thesis we have developed such estimation and correction methods that have become progressively more complicated and more accurate giving successively better correction results. A process of estimation of downwelling attenuation coefficients from multi or hyper spectral data is a basis for automatic colour restoration of underwater taken images. The results indicate that for each diving site the unique and precise coefficients can be obtained.</p><p>All standard digital cameras have built in white balancing and colour enhancement functions designed to make the images as aesthetically pleasing as possible. These functions can in most cameras not be switched off and the algorithms used are proprietary and undocumented. However, these enhancement functions can be estimated. Applying their reverse creates un-enhanced images and we show that our algorithms for underwater colour correction works significantly better when applied to such images.</p><p>Finally, we have developed a method that uses point spectra from the spectrometer together with RGB colour images from a camera to generate pseudo-hyper-spectral images. Each of these can then be colour corrected. Finally, the images can be weighted together in the proportions needed to create new correct RGB images. This method is somewhat computationally demanding but gives very encouraging results.</p><p>The algorithms and applications presented in this thesis show that automatic colour correction of underwater images can increase the credibility of data taken underwater for marine scientific purposes.</p>

<b>Objective:</b> To develop and validate computer-assisted morphometrical methods, based on stereological theory, in order to facilitate the analysis and quantitative measurements of biological tissue components. <b>Material and methods:</b> Biopsy specimens from the vaginal wall or from the vestibulum vaginae of healthy women, or from women suffering from incontinence or vestibulitis were used. A number of histochemical methods for light microscopy were used, and modified for the different morphometrical analyses. Electron microscopy was used to reveal collagen fibre diameter. Computer-assisted morphometry, based on image analysis and stereology, was employed to analyse the different tissue components in the biopsies. Computer programs for these purposes were developed and validated. <b>Results:</b> The results show that computer-assisted morphometry is of great value for quantitative measurements of the following tissue components: <b>Epithelium:</b> The epithelial structure, instead of just thickness, was measured in an unbiased way. <b>Collagen:</b> The collagen fibril diameter was determined in electron microscopic specimens, and the collagen content was analysed in light microscopic specimens. <b>Elastic fibres:</b> The amount of elastic fibres in the connective tissue was measured after visualisation by autofluorescence. <b>Vasculature:</b> A stereological method using a cycloid grid was implemented in a computer program. Healthy subjects were compared with patients suffering from vestibulitis. The results were identical in the two groups. <b>Smooth muscle:</b> A stereological method using a point grid was implemented in a computer program. Using the Delesse principle, the fibres were calculated as area fractions. The area fractions were highly variable among the different specimens. <b>Conclusion:</b> Morphometry, used correctly, is an important analysis method in histopathological research. It is important that the methods are as simple and user-friendly as possible. The present studies show that this methodology can be applied for most quantitative histological analyses.

For marine sciences sometimes there is a need to perform underwater photography. Optical properties of light cause severe quality problems for underwater photography. Light of different energies is absorbed at highly different rates under water causing significant bluishness of the images. If the colour dependent attenuation under water can be properly estimated it should be possible to use computerised image processing to colour correct digital images using Beer’s Law. In this thesis we have developed such estimation and correction methods that have become progressively more complicated and more accurate giving successively better correction results. A process of estimation of downwelling attenuation coefficients from multi or hyper spectral data is a basis for automatic colour restoration of underwater taken images. The results indicate that for each diving site the unique and precise coefficients can be obtained. All standard digital cameras have built in white balancing and colour enhancement functions designed to make the images as aesthetically pleasing as possible. These functions can in most cameras not be switched off and the algorithms used are proprietary and undocumented. However, these enhancement functions can be estimated. Applying their reverse creates un-enhanced images and we show that our algorithms for underwater colour correction works significantly better when applied to such images. Finally, we have developed a method that uses point spectra from the spectrometer together with RGB colour images from a camera to generate pseudo-hyper-spectral images. Each of these can then be colour corrected. Finally, the images can be weighted together in the proportions needed to create new correct RGB images. This method is somewhat computationally demanding but gives very encouraging results. The algorithms and applications presented in this thesis show that automatic colour correction of underwater images can increase the credibility of data taken underwater for marine scientific purposes.

<p>Digital image analysis is the field of extracting relevant information from digital images. Recent developments in imaging techniques have made 3-dimensional volume images more common. This has created a need to extend existing 2D image analysis tools to handle images of higher dimensions. Such extensions are usually not straightforward. In many cases, the theoretical and computational complexity of a problem increases dramatically when an extra dimension is added.</p><p>A fundamental problem in image analysis is image segmentation, i.e., identifying and separating relevant objects and structures in an image. Accurate segmentation is often required for further processing and analysis of the image can be applied. Despite years of active research, general image segmentation is still seen as an unsolved problem. This mainly due to the fact that it is hard to identify objects from image data only. Often, some high-level knowledge about the objects in the image is needed. This high-level knowledge may be provided in different ways. For fully automatic segmentation, the high-level knowledge must be incorporated in the segmentation algorithm itself. In interactive applications, a human user may provide high-level knowledge by guiding the segmentation process in various ways.</p><p>The aim of the work presented here is to develop segmentation and analysistools for volume images. To limit the scope, the focus has been on two specic capplications of volume image analysis: analysis of volume images of fibrousmaterials and interactive segmentation of medical images. The respective image analysis challenges of these two applications will be discussed. While the work has been focused on these two applications, many of the results presented here are applicable to other image analysis problems.</p>