MATULA, Petr, Michal KOZUBEK, Florian STAIER and Michael HAUSMANN. Precise 3D image alignment in micro-axial tomography. Journal of Microscopy, Oxford: Blackwell Science, 2003, vol. 209, No 2, p. 126-142. ISSN 0022-2720.
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Basic information
Original name Precise 3D image alignment in micro-axial tomography
Authors MATULA, Petr (203 Czech Republic, guarantor), Michal KOZUBEK (203 Czech Republic), Florian STAIER (276 Germany) and Michael HAUSMANN (276 Germany).
Edition Journal of Microscopy, Oxford, Blackwell Science, 2003, 0022-2720.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 20200 2.2 Electrical engineering, Electronic engineering, Information engineering
Country of publisher United Kingdom of Great Britain and Northern Ireland
Confidentiality degree is not subject to a state or trade secret
Impact factor Impact factor: 1.779
RIV identification code RIV/00216224:14330/03:00008544
Organization unit Faculty of Informatics
UT WoS 000181298100008
Keywords in English assignment; image fusion; image registration; Kuhn-Munkres algorithm; micro-axial tomography; resolution improvement
Tags assignment, cbia-web, image fusion, image registration, Kuhn-Munkres algorithm, micro-axial tomography, resolution improvement
Tags International impact, Reviewed
Changed by Changed by: doc. RNDr. Petr Matula, Ph.D., učo 3019. Changed: 6/5/2009 10:56.
Abstract
Micro axial tomography is a challenging technique in microscopy which improves quantitative imaging especially in cytogenetic applications by means of defined sample rotation under the microscope objective. The advantage of micro-axial tomography is an effective improvement of the precision of distance measurements between point-like objects. Under certain circumstances, the effective (3D) resolution can be improved by optimized acquisition depending on subsequent, multi-perspective image recording of the same objects followed by reconstruction methods. This requires, however, a very precise alignment of the tilted views. We present a novel feature-based image alignment method with a precision better than the full width at half maximum of the point spread function. The features are the positions (centres of gravity) of all fluorescent objects observed in the images (e.g. cell nuclei, fluorescent signals inside cell nuclei, fluorescent beads, etc.). Thus, real alignment precision depends on the localization precision of these objects. The method automatically determines the corresponding objects in subsequently tilted perspectives using a weighted bipartite graph. The optimum transformation function is computed in a least squares manner based on the coordinates of the centres of gravity of the matched objects. The theoretically feasible precision of the method was calculated using computer-generated data and confirmed by tests on real image series obtained from data sets of 200 nm fluorescent nano-particles. The advantages of the proposed algorithm are its speed and accuracy, which means that if enough objects are included, the real alignment precision is better than the axial localization precision of a single object. The alignment precision can be assessed directly from the algorithm's output. Thus, the method can be applied not only for image alignment and object matching in tilted view series in order to reconstruct (3D) images, but also to validate the experimental performance (e.g. mechanical precision of the tilting). In practice, the key application of the method is an improvement of the effective spatial (3D) resolution, because the well-known spatial anisotropy in light microscopy can be overcome. This allows more precise distance measurements between point-like objects.
Links
IBS5004010, research and development projectName: Vývoj nových diagnostických technik pro onkologii
Investor: Academy of Sciences of the Czech Republic, Programme of Targeted Research and Development Support
MSM 143300002, plan (intention)Name: Využití počítačové analýzy obrazu v optické mikroskopii
Investor: Ministry of Education, Youth and Sports of the CR, Research Intents
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