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Automatic Detection of Hypoperfused Areas in SPECT Brain Scans
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1998 (English)In: IEEE Transactions on Nuclear Science, ISSN 0018-9499, E-ISSN 1558-1578, Vol. 45, no 4, 2149-2154 p.Article in journal (Refereed) Published
Abstract [en]

Describes a method for automatic identification of areas with perfusion changes in SPECT brain images. An intersubject registration technique is used to stereotactically register images from a selected control group allowing for reference images to be created by averaging the subjects image data. An individual SPECT brain scan can be brought into registration with the reference image and comparison to the normal reference group can be made by subtracting the two volumes. Furthermore, since the variance in the reference group is known, a z-score image or an image coded in standard deviations, can be computed. The SPECT reference volume is defined in the same coordinate system as a brain atlas, and anatomical labeling of areas of interest is possible. The authors show results from the creation of an average image based on 11 individuals and from its comparison with pathological cases

Place, publisher, year, edition, pages
1998. Vol. 45, no 4, 2149-2154 p.
Keyword [en]
National Category
Information Science
URN: urn:nbn:se:uu:diva-90809DOI: 10.1109/23.708327OAI: oai:DiVA.org:uu-90809DiVA: diva2:163287
Available from: 2003-09-15 Created: 2003-09-15 Last updated: 2010-12-28Bibliographically approved
In thesis
1. Segmentation and Visualisation of Human Brain Structures
Open this publication in new window or tab >>Segmentation and Visualisation of Human Brain Structures
2003 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In this thesis the focus is mainly on the development of segmentation techniques for human brain structures and of the visualisation of such structures. The images of the brain are both anatomical images (magnet resonance imaging (MRI) and autoradigraphy) and functional images that show blood flow (functional magnetic imaging (fMRI), positron emission tomography (PET), and single photon emission tomograpy (SPECT)). When working with anatomical images, the structures segmented are visible as different parts of the brain, e.g. the brain cortex, the hippocampus, or the amygdala. In functional images, the activity or the blood flow that be seen.

Grey-level morphology methods are used in the segmentations to make tissue types in the images more homogenous and minimise difficulties with connections to outside tissue. A method for automatic histogram thresholding is also used. Furthermore, there are binary operations such as logic operation between masks and binary morphology operations.

The visualisation of the segmented structures uses either surface rendering or volume rendering. For the visualisation of thin structures, surface rendering is the better choice since otherwise some voxels might be missed. It is possible to display activation from a functional image on the surface of a segmented cortex.

A new method for autoradiographic images has been developed, which integrates registration, background compensation, and automatic thresholding to getfaster and more realible results than the standard techniques give.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2003. 68 p.
Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1104-232X ; 885
Bildanalys, segmentation, visualisation, brain, cortex, hippocampus, autoradiography, MRI, PET, ROI, Bildanalys
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Analysis
urn:nbn:se:uu:diva-3567 (URN)91-554-5729-0 (ISBN)
Public defence
2003-10-10, X, Universitetshuset, Uppsala, 10:15
Available from: 2003-09-15 Created: 2003-09-15Bibliographically approved

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