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Automated pre-processing of fluorescence microscopy data for the analysis of mitophagy.
Thesis (MEng)--Stellenbosch University, 2024.
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| Format: | Thesis |
| Language: | en_ZA en_ZA |
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Stellenbosch : Stellenbosch University
2024
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| _version_ | 1867614119329267712 |
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| access_status_str | Open Access |
| author | Batt, Richard |
| author2 | Theart, Rensu |
| author_browse | Batt, Richard Theart, Rensu |
| author_facet | Theart, Rensu Batt, Richard |
| author_sort | Batt, Richard |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MEng)--Stellenbosch University, 2024. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/130421 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA en_ZA |
| last_indexed | 2026-06-10T12:46:58.390Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/130421 Automated pre-processing of fluorescence microscopy data for the analysis of mitophagy. Batt, Richard Theart, Rensu Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering. Fluorescence microscopy Mitophagy Binary image Photodetachment threshold spectroscopy UCTD Thesis (MEng)--Stellenbosch University, 2024. ENGLISH ABSTRACT:In the context of fluorescence microscopy, the need exists for high-throughput in morphological analysis. Before engaging in high-throughput analysis of microscopy images, binarizing the images for reliable results is necessary. However, this requires the binarisation method to generalise well to a set of factors: the imaged structures within the image data set, between the conditions of different images within the set (e.g. noise), and between different data sets. Generalising effectively to just one of these factors entails testing multiple binarisation methods while optimising any required parameters and then selecting the best method. Regardless, this optimal method may generalise to merely a subset of these factors (e.g. only a few structures, only certain noise profiles). This project sought to resolve this by developing a new binarisation method that generalises better than established methods. To determine this method’s performance and generalisation ability, it was evaluated in a mitophagy use case where multiple different structures, the three organelles implicated in mitophagy, and image conditions (e.g. lighting, noise, contrast) were present. The developed method entailed using hysteresis thresholding with the low threshold estimated by applying the Kneedle algorithm. To estimate the high threshold, a range of potential values are used for the hysteresis thresholding, combined with the estimated low threshold, where the impact of the high threshold on the image is recorded as the count of foreground voxels. This produces a foreground count distribution across the possible high threshold values with this distribution’s derivative and normalised representations to optimise the generalised high threshold. This results in a method that, while not necessarily the best method for a single specific image, produces consistent and reliably usable results that are generalised effectively to a range of different organelle structure images regardless of the noise. This is supported by a visual analysis (how the binarisation looks) and a metric analysis of the developed method against established automated global and local thresholding methods. AFRIKAANSE OPSOMMING: In die konteks van fluoressensiemikroskopie bestaan die behoefte vir ho¨e deurset in morfologiese analise. Voordat ho¨e-deurset-analise van mikroskopiebeelde benaderword, is die binarisering van die beelde vir betroubare resultate nodig. Dit vereis egter dat die binariseringsmetode goed veralgemeen na ’n stel faktore: die beeldstrukture binne die beelddatastel, tussen die toestande van verskillende beelde binne die stel (bv. geraas), en tussen verskillende datastelle. Om effektief na net een van hierdie faktore te veralgemeen, behels die toets van veelvuldige binarisasiemetodes terwyl enige vereiste parameters geoptimeer word en dan die beste metode gekies word. Ongeag, hierdie optimale metode kan veralgemeen na slegs ’n subset van hierdie faktore (bv. slegs ’n paar strukture, slegs sekere geraasprofiele). Hierdie projek het gepoog om dit op te los deur ’n nuwe binarisasiemetode te ontwikkel wat beter as gevestigde metodes veralgemeen. Om hierdie metode se werkverrigting en veralgemeningsvermo¨e te bepaal, is dit ge¨evalueer in ’n mitofagiegebruiksgeval waar verskeie verskillende strukture, die drie organelle wat by mitofagie ge¨ımpliseer is, en beeldtoestande (bv. beligting, geraas, kontras) teenwoordig was. Die ontwikkelde metode behels die gebruik van histerese drempelwaarde met die lae drempel wat deur die toepassing van die Kneedle-algoritme beraam is. Om die ho¨e drempel te skat, word ’n reeks potensi¨ele waardes vir die histerese-drempel gebruik, gekombineer met die beraamde lae drempel, waar die impak van die ho¨e drempel op die beeld aangeteken word as die telling van voorgrondvoksels. Dit produseer ’n voorgrond telling verspreiding oor die moontlike ho¨e drempelwaardes met hierdie verspreiding se afgeleide en genormaliseerde voorstellings om die algemene ho¨e drempel te optimaliseer. Dit lei tot ’n metode wat, hoewel dit nie noodwendig die beste metode vir ’n enkele spesifieke beeld is nie, konsekwente en betroubaar bruikbare resultate lewer wat effektief veralgemeen word na ’n reeks verskillende organelstruktuurbeelde, ongeag die geraas. Dit word ondersteun deur ’n visuele analise (hoe die binarisering lyk) en ’n metrieke ontleding van die ontwikkelde metode teenoor gevestigde geoutomatiseerde globale en plaaslike drempelmetodes. Masters 2024-02-29T07:28:14Z 2024-04-26T16:59:49Z 2024-02-29T07:28:14Z 2024-04-26T16:59:49Z 2024-03 Thesis https://scholar.sun.ac.za/handle/10019.1/130421 en_ZA en_ZA Stellenbosch University viii, 182 pages : illustrations. application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Fluorescence microscopy Mitophagy Binary image Photodetachment threshold spectroscopy UCTD Batt, Richard Automated pre-processing of fluorescence microscopy data for the analysis of mitophagy. |
| title | Automated pre-processing of fluorescence microscopy data for the analysis of mitophagy. |
| title_full | Automated pre-processing of fluorescence microscopy data for the analysis of mitophagy. |
| title_fullStr | Automated pre-processing of fluorescence microscopy data for the analysis of mitophagy. |
| title_full_unstemmed | Automated pre-processing of fluorescence microscopy data for the analysis of mitophagy. |
| title_short | Automated pre-processing of fluorescence microscopy data for the analysis of mitophagy. |
| title_sort | automated pre processing of fluorescence microscopy data for the analysis of mitophagy |
| topic | Fluorescence microscopy Mitophagy Binary image Photodetachment threshold spectroscopy UCTD |
| url | https://scholar.sun.ac.za/handle/10019.1/130421 |
| work_keys_str_mv | AT battrichard automatedpreprocessingoffluorescencemicroscopydatafortheanalysisofmitophagy |