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An Iterative Method for Intensity Inhomogeneity Correction based on the Grey-weighted distance transform of the magnitude spectrum
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis.
(English)Manuscript (preprint) (Other academic)
National Category
Signal Processing Medical Image Processing
Research subject
Computerized Image Processing; Computerized Image Analysis
URN: urn:nbn:se:uu:diva-158673OAI: oai:DiVA.org:uu-158673DiVA: diva2:440665
Available from: 2011-09-13 Created: 2011-09-13 Last updated: 2011-10-04
In thesis
1. Image Filtering Methods for Biomedical Applications
Open this publication in new window or tab >>Image Filtering Methods for Biomedical Applications
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Filtering is a key step in digital image processing and analysis. It is mainly used for amplification or attenuation of some frequencies depending on the nature of the application. Filtering can either be performed in the spatial domain or in a transformed domain. The selection of the filtering method, filtering domain, and the filter parameters are often driven by the properties of the underlying image. This thesis presents three different kinds of biomedical image filtering applications, where the filter parameters are automatically determined from the underlying images.

Filtering can be used for image enhancement. We present a robust image dependent filtering method for intensity inhomogeneity correction of biomedical images. In the presented filtering method, the filter parameters are automatically determined from the grey-weighted distance transform of the magnitude spectrum. An evaluation shows that the filter provides an accurate estimate of intensity inhomogeneity.

Filtering can also be used for analysis. The thesis presents a filtering method for heart localization and robust signal detection from video recordings of rat embryos. It presents a strategy to decouple motion artifacts produced by the non-rigid embryonic boundary from the heart. The method also filters out noise and the trend term with the help of empirical mode decomposition. Again, all the filter parameters are determined automatically based on the underlying signal.

Transforming the geometry of one image to fit that of another one, so called image registration, can be seen as a filtering operation of the image geometry. To assess the progression of eye disorder, registration between temporal images is often required to determine the movement and development of the blood vessels in the eye. We present a robust method for retinal image registration. The method is based on particle swarm optimization, where the swarm searches for optimal registration parameters based on the direction of its cognitive and social components. An evaluation of the proposed method shows that the method is less susceptible to becoming trapped in local minima than previous methods.

With these thesis contributions, we have augmented the filter toolbox for image analysis with methods that adjust to the data at hand.


Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2011. 61 p.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 852
Digital image analysis, Image filtering, Intensity inhomogeneity correction, Empirical mode decomposition, Particle Swarm optimization, Image registration
National Category
Medical Image Processing
Research subject
Computerized Image Processing
urn:nbn:se:uu:diva-158679 (URN)978-91-554-8155-1 (ISBN)
Public defence
2011-10-25, Room 10134, Ångströmlaboratoriet, Lägerhyddsvägen 1, Uppsala, 10:15 (English)
Available from: 2011-10-03 Created: 2011-09-13 Last updated: 2014-07-21

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