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Image mining is the application of computer-based techniques that extract and exploit information from large image sets to support human users in generating knowledge from these sources. This review focuses on biomedical applications, in particular automated imaging at the cellular level. An image database is an interactive software application that combines data management, image analysis and visual data mining. The main characteristic of such a system is a layer that represents objects within an image, and that represents a large spectrum of quantitative and semantic object features. The image analysis needs to be adapted to each particular experiment, so ‘end-user programming’ will be desirable to make the technology more widely applicable. [Copyright &y& Elsevier]

The idea of a “common artefact” is a useful metaphor for the design of CSCW systems. Our ACCT model of a common artefact describes structural elements that provide awareness about the work of others. The ACCT model identifies actors, contents, conversations, and tools as the central components of a common artefact, arranged on a shared background. The elements of a common artefact provide both a background visualization of the activity, but also permit dynamic notification of particular events. We explore this process of notification, which is composed of a selection and a presentation stage. We identify the critical factors of the process, in particular we highlight techniques related to temporal and spatial distortion. The framework helps to prepare design decisions of multi-user systems more consciously. [ABSTRACT FROM AUTHOR]

In this paper, a processing chain is proposed for enhancing larger-scale building structures in magnetometer data using the continuous wavelet transform for extracting the horizontal location, depth, and homogeneity (or shape) of subsurface objects. Even though the values estimated from our data do not clearly describe the archaeological site, they are used as a filter step for removing objects whose depth and homogeneity fall outside a predefined range. This yields a binary map, where valid points represent the horizontal source positions of magnetic anomalies. In a second step, curvilinear subsets of points are identified on a larger scale by the tensor voting framework indicating archaeological relevant structures, such as arcs and lines. Here, a point casts votes on neighbouring points and their values depend on the distance, and relative position of the points. All votes are summed up and the final value discriminates, whether a certain point (and therefore a magnetic source) is part of a salient curve (e.g. arc, line). Thresholding removes points with lower values while keeping those with larger values. Although the result shows many geometric features, it still needs to be combined with other data, e.g. from aerial photography or excavations. In combination, the extracted features continue those structures in the aerial photograph, thus expanding the knowledge about the archaeological site. [ABSTRACT FROM AUTHOR]

Microscopic thin section images are a major source of information on physical properties, crystallization processes, and the evolution of rocks. Extracting the boundaries of grains is of special interest for estimating the volumetric structure of sandstone. To deal with large datasets and to relieve the geologist from a manual analysis of images, automated methods are needed for the segmentation task. This paper evaluates the region competition framework, which also includes region merging . The procedure minimizes an energy functional based on the Minimum Description Length (MDL) principle. To overcome some known drawbacks of current algorithms, we present an extension of MDL-based region merging by integrating edge information between adjacent regions. In addition, we introduce a modified implementation for region competition for overcoming computational complexities when dealing with multiple competing regions. Commonly used methods are based on solving differential equations for describing the movement of boundaries, whereas our approach implements a simple updating scheme. Furthermore, we propose intensity features for reducing the amount of data. They are derived by comparing theoretical values obtained from a model function describing the intensity inside uniaxial crystals with measured data. Error, standard deviation, and phase shift between the model and intensity measurements preserve sufficient information for a proper segmentation. Additionally, identified objects are classified into quartz grains, anhydrite, and reaction fringes by these features. This grouping is, in turn, used to improve the segmentation process further. We illustrate the benefits of this approach by four samples of microscopic thin sections and quantify them in a comparison of a segmentation result and a manually obtained one. [ABSTRACT FROM AUTHOR]

Background Congenital heart diseases ( CHD) are responsible for substantial morbidity and mortality in neonates. The preliminary diagnosis often is made by noncardiologists. For this reason, there is a huge demand of training in echocardiography of CHD. This is difficult to achieve due to limited resources of specialized centers. Objective The goal of this study was to investigate the training effect of the echocardiography simulator EchoCom on trainee's ability to diagnose CHD. Design/Methods We enrolled 10 residents for simulator-based training in echocardiography of CHD. All participants were instructed on the simulator's basic handling and had one hour to scan the first 9 datasets information (ventricular septal defect, atrial septal defect, atrioventricular septal defect, Tetralogy of Fallot, transposition of great arteries, congenital corrected transposition of great arteries, common arterial trunk, hypoplastic left heart syndrome, normal anatomy) and establish a diagnosis. No help was given except for support regarding simulator related issues. Afterward, 2 rounds of structured simulator based echocardiography training focused on echocardiographic anatomy, spatial orientation, standard views, and echocardiographic anatomy of different CHD followed. All participants completed a standardized questionnaire containing 10 multiple-choice ( MC) questions focusing on basic theoretical knowledge in echocardiographic anatomy and common CHD. Results Almost all of the residents invited from the affiliated children's hospital had little (20%) or no experience (80%) in echocardiography of CHD. Their Pretest and Posttest scores showed significant improvement for both, MC test and performance test, respectively. Conclusions Our study showed that simulator-based training in echocardiography in CHD could be very effective and may assist with training outside the scope of CHD. [ABSTRACT FROM AUTHOR]

To automatically assess hair growth during cosmetic trials, incorporating parameters such as anagen-to-telogen rate, growth rate, and especially hair diameter. We designed and qualified a new and automatic phototrichogram system based on a high-resolution DSLR camera system (theoretical resolution of 2.557 μm/pixel) and modular macrolens system with fixed focus, combined with a trainable pattern recognition software for automated analysis. We improved the standard routine for dermatological phototrichogram technique to overcome inaccuracy in thickness measurements due to hair swelling by using an alternative immersion fluid, and increased the effective resolution for hair size and thickness measurement to <4 μm. After having qualified manual measurements as gold standard for the determination of hair diameters, we established a new trainable automatic picture analysis software able to locate and measure individual hairs in length and thickness even in picture series taken from the same skin area at different time points. Comparisons between manual and automatic measurements of the same hairs showed a >90% correlation, and by comparing the automatic results with manual measurements of the same images without individual hair annotation, we could find a correlation of at least 80%. According to the results and findings generated in this qualification study, we have a reliable tool now that enables us to test cosmetic products for hair treatment in a highly automated way with a sufficient degree of precision and accuracy to detect even small changes in hair diameter during cosmetic trials. [ABSTRACT FROM AUTHOR]

Abstract: Electrical borehole wall images represent micro-resistivity measurements at the borehole wall. The lithology reconstruction is often based on visual interpretation done by geologists. This analysis is very time-consuming and subjective. Different geologists may interpret the data differently. In this work, linear discriminant analysis (LDA) in combination with texture features is used for an automated lithology reconstruction of ODP (Ocean Drilling Program) borehole 1203A drilled during Leg 197. Six rock groups are identified by their textural properties in resistivity data obtained by a Formation MircoScanner (FMS). Although discriminant analysis can be used for multi-class classification, non-optimal decision criteria for certain groups could emerge. For this reason, we use a combination of 2-class (binary) classifiers to increase the overall classification accuracy. The generalization ability of the combined classifiers is evaluated and optimized on a testing dataset where a classification rate of more than 80% for each of the six rock groups is achieved. The combined, trained classifiers are then applied on the whole dataset obtaining a statistical reconstruction of the logged formation. Compared to a single multi-class classifier the combined binary classifiers show better classification results for certain rock groups and more stable results in larger intervals of equal rock type. [Copyright &y& Elsevier]