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Finding cells, finding molecules, finding patterns
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Centrum för bildanalys.
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Centrum för bildanalys. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datoriserad bildanalys.
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
Vise andre og tillknytning
2008 (engelsk)Inngår i: International Journal of Signal and Imaging Systems Engineering, ISSN 1748-0698, Vol. 1, nr 1, s. 11-17Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Many modern molecular labelling techniques result in bright point signals. Signals from molecules that are detected directly inside a cell can be captured by fluorescence microscopy. Signals representing different types of molecules may be randomly distributed in the cells or show systematic patterns, indicating that the corresponding molecules have specific, non-random localisations and functions in the cell. Assessing this information requires high speed robust image segmentation followed by signal detection, and finally, pattern analysis. We present and discuss these types of methods and show an example of how the distribution of different variants of mitochondrial DNA can be analysed.

sted, utgiver, år, opplag, sider
2008. Vol. 1, nr 1, s. 11-17
Emneord [en]
mass data analysis, image analysis, cytometry, single molecule detection, padlock probes, pattern analysis
HSV kategori
Forskningsprogram
Datoriserad bildanalys
Identifikatorer
URN: urn:nbn:se:uu:diva-97427DOI: 10.1504/IJSISE.2008.017768OAI: oai:DiVA.org:uu-97427DiVA, id: diva2:172376
Tilgjengelig fra: 2008-08-29 Laget: 2008-08-29 Sist oppdatert: 2018-01-13bibliografisk kontrollert
Inngår i avhandling
1. Methods and models for 2D and 3D image analysis in microscopy, in particular for the study of muscle cells
Åpne denne publikasjonen i ny fane eller vindu >>Methods and models for 2D and 3D image analysis in microscopy, in particular for the study of muscle cells
2008 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Alternativ tittel[sv]
Metoder och modeller för två- och tredimensionell bildanalys inom mikroskopi, speciellt med inrikting mot muskelceller
Abstract [en]

Many research questions in biological research lead to numerous microscope images that need to be evaluated. Here digital image cytometry, i.e., quantitative, automated or semi-automated analysis of the images is an important rapidly growing discipline. This thesis presents contributions to that field. The work has been carried out in close cooperation with biomedical research partners, successfully solving real world problems.

The world is 3D and modern imaging methods such as confocal microscopy provide 3D images. Hence, a large part of the work has dealt with the development of new and improved methods for quantitative analysis of 3D images, in particular fluorescently labeled skeletal muscle cells.

A geometrical model for robust segmentation of skeletal muscle fibers was developed. Images of the multinucleated muscle cells were pre-processed using a novel spatially modulated transform, producing images with reduced complexity and facilitating easy nuclei segmentation. Fibers from several mammalian species were modeled and features were computed based on cell nuclei positions. Features such as myonuclear domain size and nearest neighbor distance, were shown to correlate with body mass, and femur length. Human muscle fibers from young and old males, and females, were related to fiber type and extracted features, where myonuclear domain size variations were shown to increase with age irrespectively of fiber type and gender.

A segmentation method for severely clustered point-like signals was developed and applied to images of fluorescent probes, quantifying the amount and location of mitochondrial DNA within cells. A synthetic cell model was developed, to provide a controllable golden standard for performance evaluation of both expert manual and fully automated segmentations. The proposed method matches the correctness achieved by manual quantification.

An interactive segmentation procedure was successfully applied to treated testicle sections of boar, showing how a common industrial plastic softener significantly affects testosterone concentrations.

sted, utgiver, år, opplag, sider
Uppsala: Acta Universitatis Upsaliensis, 2008. s. 76
Serie
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 544
Emneord
medical image analysis, image segmentation, fluorescence microscopy, cytometry, human skeletal muscle
HSV kategori
Identifikatorer
urn:nbn:se:uu:diva-9201 (URN)978-91-554-7255-9 (ISBN)
Disputas
2008-09-19, Polhemsalen, Ångströmlaboratoriet, Polacksbacken, Uppsala, 13:15
Opponent
Veileder
Tilgjengelig fra: 2008-08-29 Laget: 2008-08-29 Sist oppdatert: 2013-07-03bibliografisk kontrollert

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