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  • 1.
    Azar, Jimmy
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Busch, Christer
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Carlbom, Ingrid
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Microarray Core Detection by Geometric Restoration2012In: Analytical Cellular Pathology, ISSN 0921-8912, E-ISSN 1878-3651, Vol. 35, no 5-6, p. 381-393Article in journal (Refereed)
    Abstract [en]

    Whole-slide imaging of tissue microarrays (TMAs) holds the promise of automated image analysis of a large number of histopathological samples from a single slide. This demands high-throughput image processing to enable analysis of these tissue samples for diagnosis of cancer and other conditions. In this paper, we present a completely automated method for the accurate detection and localization of tissue cores that is based on geometric restoration of the core shapes without placing any assumptions on grid geometry. The method relies on hierarchical clustering in conjunction with the Davies-Bouldin index for cluster validation in order to estimate the number of cores in the image wherefrom we estimate the core radius and refine this estimate using morphological granulometry. The final stage of the algorithm reconstructs circular discs from core sections such that these discs cover the entire region of each core regardless of the precise shape of the core. The results show that the proposed method is able to reconstruct core locations without any evidence of localization error. Furthermore, the algorithm is more efficient than existing methods based on the Hough transform for circle detection. The algorithm's simplicity, accuracy, and computational efficiency allow for automated high-throughput analysis of microarray images.

  • 2.
    Choi, Heung-Kook
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Jarkrans, Torsten
    Bengtsson, Ewert
    Vasko, Janus
    Wester, Kenneth
    Malmstrom, Per-Uno
    Busch, Christer
    Image analysis based grading of bladder carcinoma. Comparison of object, texture and graph based methods and their reproducibility1997In: Analytical Cellular Pathology, ISSN 0921-8912, E-ISSN 1878-3651, Vol. 15, no 1, p. 1-18Article in journal (Refereed)
    Abstract [en]

    The possibility that computerized image analysis could increase the reproducibility of grading of bladder carcinoma as compared to conventional subjective grading made by pathologists was investigated. Object, texture and graph based analysis were carried

  • 3.
    Frimmel, Hans
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Egevad, Lars
    Busch, Christer
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Automatic registration and error detection of multiple slices using landmarks2001In: Analytical Cellular Pathology, ISSN 0921-8912, E-ISSN 1878-3651, Vol. 23, p. 159-165Article in journal (Refereed)
  • 4.
    Jarkrans, Torsten
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Vasko, Janos
    Bengtsson, Ewert
    Choi, Heung-Kook
    Malmström, Per-Uno
    Wester, Kenneth
    Busch, Christer
    Grading of transitional cell bladder carcinoma by image analysis of histological sections  1995In: Analytical Cellular Pathology, ISSN 0921-8912, E-ISSN 1878-3651, Vol. 8, no 2, p. 135-158Article in journal (Refereed)
    Abstract [en]

    Image analysis of histological sections was used to achieve a more objective malignancy grading of transitional cell carcinoma of the bladder. Images from Feulgen-stained sections from a clinical material of 197 tumours were analyzed. Features at various

  • 5.
    Ranefall, Petter
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Egevad, Lars
    Nordin, Bo
    Bengtsson, Ewert
    A new method for segmentation of colour images applied to immunohistochemically stained cell nuclei1997In: Analytical Cellular Pathology, ISSN 0921-8912, E-ISSN 1878-3651, Vol. 15, no 3, p. 145-156Article in journal (Refereed)
    Abstract [en]

    A new method for segmenting images of immunohistochemically stained cell nuclei is presented. The aim is to distinguish between cell nuclei with a positive staining reaction and other cell nuclei, and to make it possible to quantify the reaction. First, a

  • 6.
    Ranefall, Petter
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wester, Kenneth
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Andersson, Ann-Catrin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Busch, Christer
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Automatic quantification of immunohistochemically stained cell nuclei based on standard reference cells1998In: Analytical Cellular Pathology, ISSN 0921-8912, E-ISSN 1878-3651, Vol. 17, no 2, p. 111-23Article in journal (Refereed)
    Abstract [en]

    A fully automatic method for quantification of images of immunohistochemically stained cell nuclei by computing area proportions, is presented. Agarose embedded cultured fibroblasts were fixed, paraffin embedded and sectioned at 4 microm. They were then stained together with 4 microm sections of the test specimen obtained from bladder cancer material. A colour based classifier is automatically computed from the control cells. The method was tested on formalin fixed paraffin embedded tissue section material, stained with monoclonal antibodies against the Ki67 antigen and cyclin A protein. Ki67 staining results in a detailed nuclear texture with pronounced nucleoli and cyclin A staining is obtained in a more homogeneously distributed pattern. However, different staining patterns did not seem to influence labelling index quantification, and the sensitivity to variations in light conditions and choice of areas within the control population was low. Thus, the technique represents a robust and reproducible quantification method. In tests measuring proportions of stained area an average standard deviation of about 1.5% for the same field was achieved when classified with classifiers created from different control samples.

  • 7.
    Ranefall, Petter
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wester, Kenneth
    Bengtsson, Ewert
    Automatic quantification of immunohistochemically stained cell nuclei using unsupervised image analysis1998In: Analytical Cellular Pathology, ISSN 0921-8912, E-ISSN 1878-3651, Vol. 16, no 1, p. 29-43Article in journal (Refereed)
    Abstract [en]

    A method for quantification of images of immunohistochemically stained cell nuclei by computing area proportions is presented. The image is transformed by a principal component transform. The resulting first component image is used to segment the objects

  • 8.
    Ranefall, Petter
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wester, Kenneth
    Busch, Christer
    Malmström, Per-Uno
    Bengtsson, Ewert
    Automatic quantification of microvessels using unsupervised image analysis1998In: Analytical Cellular Pathology, ISSN 0921-8912, E-ISSN 1878-3651, Vol. 17, no 2, p. 83-92Article in journal (Refereed)
    Abstract [en]

    An automatic method for quantification of images of microvessels by computing area proportions and number of objects is presented. The objects are segmented from the background using dynamic thresholding of the average component size histogram. To be able

  • 9.
    Wählby, Carolina
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Vondrus, Mikael
    Bengtsson, Ewert
    Björkesten, Lennart
    Algorithms for cytoplasm segmentation of fluorescence labeled cells2002In: Analytical Cellular Pathology, ISSN 0921-8912, E-ISSN 1878-3651, Vol. 24, no 2-3, p. 101-111Article in journal (Refereed)
    Abstract [en]

    Automatic cell segmentation has various applications in cytometry, and while the nucleus is often very distinct and easy to identify, the cytoplasm provides a lot more challenge. A new combination of image analysis algorithms for segmentation of cells imaged by fluorescence microscopy is presented. The algorithm consists of an image pre-processing step, a general segmentation and merging step followed by a segmentation quality measurement. The quality measurement consists of a statistical analysis of a number of shape descriptive features. Objects that have features that differ to that of correctly segmented single cells can be further processed by a splitting step. By statistical analysis we therefore get a feedback system for separation of clustered cells. After the segmentation is completed, the quality of the final segmentation is evaluated. By training the algorithm on a representative set of training images, the algorithm is made fully automatic for subsequent images created under similar conditions. Automatic cytoplasm segmentation was tested on CHO-cells stained with calcein. The fully automatic method showed between 89% and 97% correct segmentation as compared to manual segmentation.

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