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Histogram thresholding using kernel density estimates
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.
2000 (English)In: In Proceedings of the Swedish Society for Automated Image Analysis (SSAB) Symposium on Image Analysis, Halmstad, Sweden, 41-44 p.Article in journal (Refereed) Published
Place, publisher, year, edition, pages
2000. 41-44 p.
URN: urn:nbn:se:uu:diva-90089OAI: oai:DiVA.org:uu-90089DiVA: diva2:162267
Available from: 2002-12-19 Created: 2002-12-19 Last updated: 2010-03-01Bibliographically approved
In thesis
1. Development of Algorithms for Digital Image Cytometry
Open this publication in new window or tab >>Development of Algorithms for Digital Image Cytometry
2002 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis presents work in digital image cytometry applied to fluorescence microscope images of cultivated cells. Focus has been on the development and compilation of robust image analysis tools, enabling quantitative measurements of various properties of cells and cell structures. A significant part of the work has consisted of developing robust segmentation methods for fluorescently labelled cells. This, in combination with effort applied in the areas of feature extraction and statistical data analysis, has enabled the compilation of a complete chain of processing steps to produce a system capable of performing fully automatic segmentation and classification of fluorescently labelled cells according to their level of activation.

Two sequences of processing steps, both leading to automatic cytoplasm segmentation of fluorescence microscopy cell images are presented. In one of the sequences, an additional image of the nuclei of the cells is segmented. The nuclei are then used as seeds for the segmentation of the cytoplasm image. This solves the problem of over-segmentation of the cytoplasms in an efficient way. The other sequence uses merge and split algorithms on the cytoplasm image, in conjunction with statistical analysis of descriptive features. This analysis is used in a feedback system to improve the segmentation performance, and to give an overall quality measure of the segmentation.

A classification method that separates individual cells into three classes, depending on their level of activation, is described. The method is based on analysis of time series of images. Using both general purpose features and carefully designed problem specific features, in combination with a floating feature selection procedure, a Bayesian classifier is built. Evaluation showed that the performance of the fully automatic classification procedure was very close to the performance of skilled manual classification.

A novel method for performing estimation of intensity nonuniformites of microscope images is presented. Methods to solve many other problems related to image analysis of cell images are discussed and evaluated. All methods presented in this work are applicable to real-world situations. The two main projects of the thesis work have been performed in close cooperation with and according to demands of the biomedical industry.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2002. 67 p.
Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1104-232X ; 789
Bildanalys, Bildanalys
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Analysis
urn:nbn:se:uu:diva-3239 (URN)91-554-5497-6 (ISBN)
Public defence
2003-01-17, Häggsalen (room 10132), Ångströmlaboratoriet, Uppsala, 10:15
Available from: 2002-12-19 Created: 2002-12-19Bibliographically approved

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