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  • 1.
    Andersson, Viktor
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Biomolecular and Organic Electronics. Linköping University, The Institute of Technology.
    Masich, Sergej
    Department of cell and molecular biology, Karolinska institutet, Stockholm.
    Solin, Niclas
    Linköping University, Department of Physics, Chemistry and Biology, Biomolecular and Organic Electronics. Linköping University, The Institute of Technology.
    Inganäs, Olle
    Linköping University, Department of Physics, Chemistry and Biology, Biomolecular and Organic Electronics. Linköping University, The Institute of Technology.
    Morphology of organic electronic materials imaged via electron tomography2012In: Journal of Microscopy, ISSN 0022-2720, E-ISSN 1365-2818, Vol. 247, no 3, p. 277-287Article in journal (Refereed)
    Abstract [en]

    Several organic materials and blends have been studied with the use of electron tomography. Tomography reconstructions of active layers of organic solar cells, where various preparation techniques have been used, have been analysed and compared to device behaviour. In addition, materials with predefined structures, including contrast enhancing features, have been studied and double tilt data collection has been employed to improve reconstructions. Small changes in preparation procedures may lead to large differences in morphology and device performance, and the results also indicate a complex relation between these.

  • 2.
    Immerstrand, Charlotte
    et al.
    Linköping University, Department of Molecular and Clinical Medicine, Medical Microbiology. Linköping University, Faculty of Health Sciences.
    Hedlund, Joel
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, The Institute of Technology.
    Magnusson, Karl-Eric
    Linköping University, Department of Molecular and Clinical Medicine, Medical Microbiology. Linköping University, Faculty of Health Sciences.
    Sundqvist, Tommy
    Linköping University, Department of Molecular and Clinical Medicine, Medical Microbiology. Linköping University, Faculty of Health Sciences.
    Holmgren-Peterson, Kajsa
    Linköping University, Department of Molecular and Clinical Medicine, Medical Microbiology. Linköping University, Faculty of Health Sciences.
    Organelle transport in melanophores analyzed by white light image correlation spectroscopy2007In: Journal of Microscopy, ISSN 0022-2720, E-ISSN 1365-2818, Vol. 225, no 3, p. 275-282Article in journal (Refereed)
    Abstract [en]

    Intracellular transport of organelles, vesicles and proteins is crucial in all eukaryotic cells, and is accomplished by motor proteins that move along cytoskeletal filaments. A widely used model of intracellular transport is Xenopus laevis melanophores. These cells help the frog to change color by redistributing melanin-containing organelles in the cytoplasm. The high contrast of the pigment organelles permits changes in distribution to be observed by ordinary light microscopy; other intracellular transport systems often require fluorescence labeling. Here we have developed white light Image Correlation Spectroscopy (ICS) to monitor aggregation and dispersion of pigment. Hitherto in ICS, images of fluorescent particles from Confocal Laser Scanning Microscopy (CLSM) have been used to calculate autocorrelation functions from which the density can be obtained. In the present study we show that ICS can be modified to enable analysis of light-microscopy images; it can be used to monitor pigment aggregation and dispersion, and distinguish between different stimuli. This new approach makes ICS applicable not only to fluorescent but also to black-and-white images from light or electron microscopy, and is thus very versatile in different studies of movement of particles on the membrane or in the cytoplasm of cells without potentially harmful fluorescence labeling and activation.

  • 3.
    Spiecker, E.
    et al.
    University of Kiel, Germany.
    Garbrecht, Magnus
    Linköping University, Department of Physics, Chemistry and Biology, Thin Film Physics. Linköping University, Faculty of Science & Engineering.
    Jaeger, W.
    University of Kiel, Germany.
    Tillmann, K.
    Forschungszentrum Julich, Germany; Forschungszentrum Julich, Germany.
    Advantages of aberration correction for HRTEM investigation of complex layer compounds2010In: Journal of Microscopy, ISSN 0022-2720, E-ISSN 1365-2818, Vol. 237, no 3, p. 341-346Article in journal (Refereed)
    Abstract [en]

    Aberration-corrected high-resolution transmission electron microscopy (HRTEM) has been applied to resolve the atomic structure of a complex layered crystal, (PbS)(1.14)NbS(2), which comprises a high density of incommensurate interfaces. The strong suppression of image delocalization and the favourable contrast transfer under negative C(s) imaging (NCSI) conditions have been exploited for obtaining HRTEM images which directly reveal the projected crystal structure and allow to study lattice imperfections, like stacking disorder and layer undulations, with atomic scale resolution. The advantages of aberration-corrected HRTEM over conventional HRTEM are demonstrated by direct comparison of experimental images and computer simulations.

  • 4.
    Su, R.
    et al.
    Tianjin University, Peoples R China.
    Zhang, C.
    CSIRO Data61, Australia; University of New South Wales, Australia.
    Pham, Tuan
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Davey, R.
    CSIRO Food and Nutr, Australia.
    Bischof, L.
    CSIRO Data61, Australia.
    Vallotton, P.
    CSIRO Data61, Australia; ETH, Switzerland.
    Lovell, D.
    CSIRO Data61, Australia; Queensland University of Technology, Australia.
    Hope, S.
    CSIRO Food and Nutr, Australia.
    Schmoelzl, S.
    CSIRO Food and Nutr, Australia.
    Sun, C.
    CSIRO Data61, Australia.
    Detection of tubule boundaries based on circular shortest path and polar-transformation of arbitrary shapes2016In: Journal of Microscopy, ISSN 0022-2720, E-ISSN 1365-2818, Vol. 264, no 2, p. 127-142Article in journal (Refereed)
    Abstract [en]

    In studies of germ cell transplantation, counting cells and measuring tubule diameters from different populations using labelled antibodies are important measurement processes. However, it is slow and sanity grinding to do these tasks manually. This paper proposes a way to accelerate these processes using a new image analysis framework based on several novel algorithms: centre points detection of tubules, tubule shape classification, skeleton-based polar-transformation, boundary weighting of polar-transformed image, and circular shortest path smoothing. The framework has been tested on a dataset consisting of 27 images which contain a total of 989 tubules. Experiments show that the detection results of our algorithm are very close to the results obtained manually and the novel approach can achieve a better performance than two existing methods. Lay description In studies of germ cell transplantation, counting cells and measuring tubule diameters from different populations using labelled antibodies are important measurement processes. However, it is slow and sanity grinding to do these tasks manually. This paper proposes a way to accelerate these processes using a new image analysis framework based on several novel algorithms: center points detection of tubules, tubule shape classification, skeleton based polar-transformation, boundary weighting of polar-transformed image, and circular shortest path smoothing. The framework has been tested on a dataset consisting of 27 images which contain a total of 989 tubules. Experiments show that the detection results of our algorithm are very close to the results obtained manually and the novel approach can achieve a better performance than two existing methods.

  • 5.
    Zhang, C
    et al.
    North Ryde, New South Wales 2113, Australia.
    Sun, C
    The University of New South Wales, Canberra,Australia.
    Pham, Tuan D.
    ‡Aizu Research Cluster for Medical Engineering and Informatics, Research Center for AdvancedInformation Science and Technology, The University of Aizu, Fukushima, Japan.
    Segmentation of clustered nuclei based on concave curve expansion2013In: Journal of Microscopy, ISSN 0022-2720, E-ISSN 1365-2818, ISSN 1365-2818, Vol. 251, no 1, p. 57-67Article in journal (Refereed)
    Abstract [en]

    Segmentation of nuclei from images of tissue sections is important for many biological and biomedical studies. Many existing image segmentation algorithms may lead to oversegmentation or undersegmentation for clustered nuclei images. In this paper, we proposed a new image segmentation algorithm based on concave curve expansion to correctly and accurately extract markers from the original images. Marker-controlled watershed is then used to segment the clustered nuclei. The algorithm was tested on both synthetic and real images and better results are achieved compared with some other state-of-the-art methods.

  • 6.
    Zhang, C
    et al.
    CSIRO Mathematics, Informatics and Statistics Division, North Ryde, New South Wales, Australia; School of Engineering and Information Technology, The University of New South Wales, Canberra, ACT, Australia.
    Sun, C
    CSIRO Mathematics, Informatics and Statistics Division, North Ryde, New South Wales, Australia.
    Pham, Tuan D
    Aizu Research Cluster for Medical Engineering and Informatics, Research Center for Advanced Information Science and Technology, The University of Aizu, Fukushima, Japan.
    Segmentation of clustered nuclei based on concave curve expansion2013In: Journal of Microscopy, ISSN 0022-2720, E-ISSN 1365-2818, Vol. 251, no 1, p. 57-67Article in journal (Refereed)
    Abstract [en]

    Segmentation of nuclei from images of tissue sections is important for many biological and biomedical studies. Many existing image segmentation algorithms may lead to oversegmentation or undersegmentation for clustered nuclei images. In this paper, we proposed a new image segmentation algorithm based on concave curve expansion to correctly and accurately extract markers from the original images. Marker-controlled watershed is then used to segment the clustered nuclei. The algorithm was tested on both synthetic and real images and better results are achieved compared with some other state-of-the-art methods.

  • 7.
    Zhang, Chao
    et al.
    School of Engineering and Information Technology, The University of New South Wales, Canberra, Australia; CSIRO Computational Informatics, North Ryde, Australia.
    Sun, Changming
    CSIRO Computational Informatics, North Ryde, Australia.
    Su, Ran
    Bioinformatics Institute, Matrix, Singapore.
    Pham, Tuan D
    Aizu Research Cluster for Medical Engineering and Informatics, Research Center for Advanced Information Science and Technology, The University of Aizu, Fukushima, Japan.
    Clustered nuclei splitting via curvature information and gray-scale distance transform2015In: Journal of Microscopy, ISSN 0022-2720, E-ISSN 1365-2818, Vol. 259, no 1, p. 36-52Article in journal (Refereed)
    Abstract [en]

    Clusters or clumps of cells or nuclei are frequently observed in two dimensional images of thick tissue sections. Correct and accurate segmentation of overlapping cells and nuclei is important for many biological and biomedical applications. Many existing algorithms split clumps through the binarization of the input images; therefore, the intensity information of the original image is lost during this process. In this paper, we present a curvature information, gray scale distance transform, and shortest path splitting line-based algorithm which can make full use of the concavity and image intensity information to find out markers, each of which represents an individual object, and detect accurate splitting lines between objects using shortest path and junction adjustment. The proposed algorithm is tested on both synthetic and real nuclei images. Experiment results show that the performance of the proposed method is better than that of marker-controlled watershed method and ellipse fitting method.

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