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Analysis of 3D images of molecules, cells, tissues and organs
Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
2007 (English)In: Medicinteknikdagarna 2007, 2007, 1- p.Conference paper (Other scientific)
Abstract [en]

Our world is three dimensional. With our eyes we mainly see the surfaces of 3D objects and in conventional imaging we see projections of parts of the 3D world down to 2D. But over the last decades new imaging techniques such as tomography and confocal microscopy have evolved that make true 3D volume images available,. These images can reveal information about the inner properties and conditions of objects, e.g. our bodies, that can be of immense value to science and medicine. But to really explore the information in these images we need computer support.

At the Centre for Image Analysis in Uppsala we are developing methods for the analysis and visualisation of volume images. A nice aspect of image processing methods is that they in most cases are independent of the scale in the images. In this presentation we will give examples of how images of widely different scales can be analysed and visualised.

- At the highest resolution we have images of protein molecules created by cryo-electron tomography with voxels of a few nanometers.

- Using confocal microscopy we can also image single molecules, but then only seeing them as bright spots that need to be localized at micrometer scales in the cells.

- The cells build up tissue and using conventional pathology stains or micro CT we can image the tissue in 2D and 3D. We are using such images to develop methods for studying tissue integration of implants.

- Finally conventional X-ray tomography and magnetic resonance tomography provide images on the organ level with voxels in the millimetre range. We are developing methods for liver segmentation in CT data and visualising the contrast uptake over time in MR angiography images of breasts.

Place, publisher, year, edition, pages
2007. 1- p.
National Category
Computer Vision and Robotics (Autonomous Systems)
URN: urn:nbn:se:uu:diva-12708OAI: oai:DiVA.org:uu-12708DiVA: diva2:40477
Invited presentationAvailable from: 2008-01-10 Created: 2008-01-10

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