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Subject: image acquisition ×

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    Automated distinction of shearing and distortion artefacts in structured illumination microscopy
    (Washington D.C. : Optical Society of America, 2018) Förster, Ronny; Müller, Walter; Richter, Renè; Heintzmann, Rainer
    Any motion during an image acquisition leads to an artefact in the final image. Structured illumination microscopy (SIM) combines several raw images into one high-resolution image and is thus particularly prone to these motion artefacts. Their unpredictable shape cannot easily be distinguished from real high-resolution content. We previously implemented a motion detection specifically for SIM, which had two shortcomings which are solved here. First, the brightness dependency of the motion signal is removed. Second, the empirical threshold of the calculated motion signal was not a threshold at a maximum allowed artefact. Here we investigate which artefacts are still acceptable and which linear movement creates them. Thus, the motion signal is linked with the maximal strength of the expected artefact. A signal-to-noise analysis including classification successfully distinguishes between artefact-free imaging, shearing and distortion artefacts in biological specimens. A shearing, as in wide-field microscopy, is the dominant reconstruction artefact, while distortions arise not until surprisingly fast movements.
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    An automated field phenotyping pipeline for application in grapevine research
    (Basel : MDPI, 2015) Kicherer, Anna; Herzog, Katja; Pflanz, Michael; Wieland, Markus; Rüger, Philipp; Kecke, Steffen; Kuhlmann, Heiner; Töpfer, Reinhard
    Due to its perennial nature and size, the acquisition of phenotypic data in grapevine research is almost exclusively restricted to the field and done by visual estimation. This kind of evaluation procedure is limited by time, cost and the subjectivity of records. As a consequence, objectivity, automation and more precision of phenotypic data evaluation are needed to increase the number of samples, manage grapevine repositories, enable genetic research of new phenotypic traits and, therefore, increase the efficiency in plant research. In the present study, an automated field phenotyping pipeline was setup and applied in a plot of genetic resources. The application of the PHENObot allows image acquisition from at least 250 individual grapevines per hour directly in the field without user interaction. Data management is handled by a database (IMAGEdata). The automatic image analysis tool BIVcolor (Berries in Vineyards-color) permitted the collection of precise phenotypic data of two important fruit traits, berry size and color, within a large set of plants. The application of the PHENObot represents an automated tool for high-throughput sampling of image data in the field. The automated analysis of these images facilitates the generation of objective and precise phenotypic data on a larger scale.