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- W1658864987 abstract "One of the most frequent ways to interact with the surrounding environment occursas a visual way. Hence imaging is a very common way in order to gain informationand learn from the environment. Particularly in the field of cellular biology, imagingis applied in order to get an insight into the minute world of cellular complexes. Asa result, in recent years many researches have focused on developing new suitableimage processing approaches which have facilitates the extraction of meaningfulquantitative information from image data sets. In spite of recent progress, but due tothe huge data set of acquired images and the demand for increasing precision, digitalimage processing and statistical analysis are gaining more and more importance inthis field.There are still limitations in bioimaging techniques that are preventing sophisticatedoptical methods from reaching their full potential. For instance, in the 3DElectron Microscopy(3DEM) process nearly all acquired images require manual postprocessingto enhance the performance, which should be substitute by an automaticand reliable approach (dealt in Part I). Furthermore, the algorithms to localize individualfluorophores in 3D super-resolution microscopy data are still in their initialphase (discussed in Part II). In general, biologists currently lack automated and highthroughput methods for quantitative global analysis of 3D gene structures.This thesis focuses mainly on microscopy imaging approaches based on MachineLearning, statistical analysis and image processing in order to cope and improve thetask of quantitative analysis of huge image data. The main task consists of buildinga novel paradigm for microscopy imaging processes which is able to work in anautomatic, accurate and reliable way.The specific contributions of this thesis can be summarized as follows:• Substitution of the time-consuming, subjective and laborious task of manualpost-picking in Cryo-EM process by a fully automatic particle post-pickingroutine based on Machine Learning methods (Part I).• Quality enhancement of the 3D reconstruction image due to the high performanceof automatically post-picking steps (Part I).• Developing a full automatic tool for detecting subcellular objects in multichannel3D Fluorescence images (Part II).• Extension of known colocalization analysis by using spatial statistics in orderto investigate the surrounding point distribution and enabling to analyze thecolocalization in combination with statistical significance (Part II).All introduced approaches are implemented and provided as toolboxes which arefree available for research purposes." @default.
- W1658864987 created "2016-06-24" @default.
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- W1658864987 date "2014-02-07" @default.
- W1658864987 modified "2023-09-27" @default.
- W1658864987 title "Automatic approaches for microscopy imaging based on machine learningand spatial statistics" @default.
- W1658864987 hasPublicationYear "2014" @default.
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