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Especially for investigator-initiated research at universities and academic institutions, Internet-based rare disease registries (RDR) are required that integrate electronic data capture (EDC) with automatic image analysis or manual image annotation. We propose a modular framework merging alpha-numerical and binary data capture. In concordance with the Office of Rare Diseases Research recommendations, a requirement analysis was performed based on several RDR databases currently hosted at Uniklinik RWTH Aachen, Germany. With respect to the study management tool that is already successfully operating at the Clinical Trial Center Aachen, the Google Web Toolkit was chosen with Hibernate and Gilead connecting a MySQL database management system. Image and signal data integration and processing is supported by Apache Commons FileUpload-Library and ImageJ-based Java code, respectively. As a proof of concept, the framework is instantiated to the German Calciphylaxis Registry. The framework is composed of five mandatory core modules: (1) Data Core, (2) EDC, (3) Access Control, (4) Audit Trail, and (5) Terminology as well as six optional modules: (6) Binary Large Object (BLOB), (7) BLOB Analysis, (8) Standard Operation Procedure, (9) Communication, (10) Pseudonymization, and (11) Biorepository. Modules 1-7 are implemented in the German Calciphylaxis Registry. The proposed RDR framework is easily instantiated and directly integrates image management and analysis. As open source software, it may assist improved data collection and analysis of rare diseases in near future. [ABSTRACT FROM AUTHOR]

Bark beetle dispersal and host selection behaviour are a complex and poorly understood process, resulting in specific spatio-temporal infestation patterns in forests. Aerial images from the Bavarian Forest National Park (Germany) provide a high-resolution, that is, tree-scale data set for the period 2001-2010, including information about Ips typographus (Col., Curculio., Scolytinae) infestation, the application of sanitary logging, natural forest edges and the area of living spruce susceptible to bark beetle infestation. We combined methods of GIS and image analysis to investigate the infestation probabilities at three types of forest edges under spatial and temporal aspects and compared them to the corresponding probabilities at the stand interior. Our results showed a pronounced infestation predisposition of such edge trees delimiting infestation patches cleared by sanitary logging measures, in particular at the south-facing edge sector. In contrast, edges adjacent to non-cleared infestation were revealed as less attractive for subsequent infestations, but nonetheless more attractive than permanent forest edges or the stand interior. Additionally, we measured near-bark surface air temperature to determine microclimatic differences at those edge- or non-edge sites and related them to predisposition results. Finally, our study emphasized favourable microclimatic conditions-summarized as the 'sun-effect'-as a decisive factor enhancing the local infestation probability at recent forest edges in multiple ways. Both insect- and host tree-related reactions to suddenly altered microclimate are supposed to bias arbitrary colonization behaviour at patch and tree level, thereby mainly explaining observed infestation patterns. From the forester's point of view, our results may contribute to precise bark beetle risk assessment and thus facilitate decision making in forest management. [ABSTRACT FROM AUTHOR]

Many technologies in precision agriculture (PA) require image analysis and image- processing with weed and background differentiations. The detection of weeds on mulched cropland is one important image-processing task for sensor based precision herbicide applications. The article introduces a special vegetation index, the Difference Index with Red Threshold (DIRT), for the weed detection on mulched croplands. Experimental investigations in weed detection on mulched areas point out that the DIRT performs better than the Normalized Difference Vegetation Index (NDVI). The result of the evaluation with four different decision criteria indicate, that the new DIRT gives the highest reliability in weed/background differentiation on mulched areas. While using the same spectral bands (infrared and red) as the NDVI, the new DIRT is more suitable for weed detection than the other vegetation indices and requires only a small amount of additional calculation power. The new vegetation index DIRT was tested on mulched areas during automatic ratings with a special weed camera system. The test results compare the new DIRT and three other decision criteria: the difference between infrared and red intensity (Diff), the soil-adjusted quotient between infrared and red intensity (Quotient) and the NDVI. The decision criteria were compared with the definition of a worse case decision quality parameter Q, suitable for mulched croplands. Although this new index DIRT needs further testing, the index seems to be a good decision criterion for the weed detection on mulched areas and should also be useful for other image processing applications in precision agriculture. The weed detection hardware and the PC program for the weed image processing were developed with funds from the German Federal Ministry of Education and Research (BMBF). [ABSTRACT FROM AUTHOR]

The article reflects on the 5th International Conference on Scale-Space and PDE Methods in Computer Vision. It states that the scale-space conference was held at Schlösschen Schönburg in Hofgeismar, Germany. The conference reviewed papers relating on scale-space and pick six papers that received the best reviews and were published in the current issue of the International Journal of Computer Vision.