Development of Algorithms for Digital Image Cytometry

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…


1 Introduction
2 Background
2.1.2Image cytometry
2.1.3Digital microscopy
2.1.4Confocal microscopy
2.1.5Fluorescence microscopy
2.2 Digital image analysis
2.2.1 Digital image
2.2.2 Image analysis methodology
3 Projects
4 Material and Methods
4.1Sample preparation
4.2Image acquisition
4.3.1 INU estimation using additional images
4.3.2 Data-driven INU estimation
4.4 Image segmentation
4.4.1 Segmentation into foreground and background
4.4.2Object separation
4.4.3Merge and split
4.4.4 Segmentation of nuclei
4.4.5 Segmentation of cytoplasms
4.5Feature extraction
4.5.1Regionsof interest
4.5.2Selectedgeneral features
4.8 Implementation
6 Concluding remarks
Brief summary of enclosed papers

Author: Lindblad, Joakim

Source: Uppsala University Library

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