Detection of fluorescently labeled particles in Escherichia coli
Annila, Teppo (2015)
2015
Master's Degree Programme in Information Technology
Tieto- ja sähkötekniikan tiedekunta - Faculty of Computing and Electrical Engineering
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Hyväksymispäivämäärä
2015-12-09
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tty-201511241780
https://urn.fi/URN:NBN:fi:tty-201511241780
Tiivistelmä
Escherichia coli are one of the most commonly used bacteria to study important biolog-ical processes such as transcription and translation. This is due to its simple structure and gene expression system, as well as the easiness to maintain live cultures in a laboratory environment. Due to recent developments in fluorescence microscopy and fluorescence labeling, it is now possible to study such biological processes in live cells at single cell and single molecule level. When analyzing such biological processes, the detection of fluorescent objects and subcellular particles is usually one of the first tasks providing important information for subsequent data analysis.
Although many algorithms have been proposed for the task, it still remains a challenge due to the limitations of image acquisition when imaging live cells. For example, the intensity of the illumination light and the exposure time is usually minimized to prevent damage to the cells, resulting in images with low signal-to-noise ratio. Due to this and the large amount of data typically used for these studies, automated, high quality parti-cle detection algorithms are needed.
In this thesis, we present a novel method for detecting fluorescently labeled subcellular particles in Escherichia coli. The proposed method is tested in both synthetic and em-pirical images and is compared to previous, commonly used methods using standard performance evaluation metrics. The results indicate that the proposed algorithm has a good performance with all image types tested and that it outperforms the previous methods. It is also able to achieve good results with other types of cells than E. coli. Moreover, it allows a robust detection of particles from low signal-to-noise ratio images with good accuracy, thus providing accurate and unbiased results for subsequent analy-sis.
Although many algorithms have been proposed for the task, it still remains a challenge due to the limitations of image acquisition when imaging live cells. For example, the intensity of the illumination light and the exposure time is usually minimized to prevent damage to the cells, resulting in images with low signal-to-noise ratio. Due to this and the large amount of data typically used for these studies, automated, high quality parti-cle detection algorithms are needed.
In this thesis, we present a novel method for detecting fluorescently labeled subcellular particles in Escherichia coli. The proposed method is tested in both synthetic and em-pirical images and is compared to previous, commonly used methods using standard performance evaluation metrics. The results indicate that the proposed algorithm has a good performance with all image types tested and that it outperforms the previous methods. It is also able to achieve good results with other types of cells than E. coli. Moreover, it allows a robust detection of particles from low signal-to-noise ratio images with good accuracy, thus providing accurate and unbiased results for subsequent analy-sis.