uu.seUppsala University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Segmentation and Visualisation of Human Brain Structures
Uppsala University, Interfaculty Units, Centre for Image Analysis.
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. , p. 68
Series
Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1104-232X ; 885
Keywords [en]
Bildanalys, segmentation, visualisation, brain, cortex, hippocampus, autoradiography, MRI, PET, ROI
Keywords [sv]
Bildanalys
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Analysis
Identifiers
URN: urn:nbn:se:uu:diva-3567ISBN: 91-554-5729-0 (print)OAI: oai:DiVA.org:uu-3567DiVA, id: diva2:163295
Public defence
2003-10-10, X, Universitetshuset, Uppsala, 10:15
Opponent
Supervisors
Available from: 2003-09-15 Created: 2003-09-15 Last updated: 2018-01-13Bibliographically approved
List of papers
1. Surface Construction Especially Suited for Visualisation of Thin Structures
Open this publication in new window or tab >>Surface Construction Especially Suited for Visualisation of Thin Structures
1997 (English)In: Proceedings of 10th Scandinavian Conference on Image Analysis, SCIA'97, 1997, p. 2149–2154-Chapter in book (Other academic)
Identifiers
urn:nbn:se:uu:diva-90808 (URN)
Available from: 2003-09-15 Created: 2003-09-15 Last updated: 2010-03-01Bibliographically approved
2. Automatic Detection of Hypoperfused Areas in SPECT Brain Scans
Open this publication in new window or tab >>Automatic Detection of Hypoperfused Areas in SPECT Brain Scans
Show others...
1998 (English)In: IEEE Transactions on Nuclear Science, ISSN 0018-9499, E-ISSN 1558-1578, Vol. 45, no 4, p. 2149-2154Article 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

Keywords
POSITRON EMISSION TOMOGRAPHY, PET IMAGES, ATLAS, LOCALIZATION, LINE
National Category
Information Systems
Identifiers
urn:nbn:se:uu:diva-90809 (URN)10.1109/23.708327 (DOI)
Available from: 2003-09-15 Created: 2003-09-15 Last updated: 2018-01-13Bibliographically approved
3. Segmentation of the Brain in MRI Using Grey Level Morphology and Propagation of Information
Open this publication in new window or tab >>Segmentation of the Brain in MRI Using Grey Level Morphology and Propagation of Information
1999 (English)In: Proceedings of 11th Scandinavian Conference on Image Analysis, SCIA’99, Kangerlussuaq, Greenland, 1999, p. 367–373-Chapter in book (Other academic)
Identifiers
urn:nbn:se:uu:diva-90810 (URN)
Available from: 2003-09-15 Created: 2003-09-15 Last updated: 2010-03-01Bibliographically approved
4. Combined Visualisation of Functional and Anatomical Brain Images
Open this publication in new window or tab >>Combined Visualisation of Functional and Anatomical Brain Images
2001 (English)In: Proceedings of 12th Scandinavian Conference on Image Analysis, SCIA 2001, Bergen, Norway, 2001, p. 84–89-Chapter in book (Other academic)
Identifiers
urn:nbn:se:uu:diva-90811 (URN)
Available from: 2003-09-15 Created: 2003-09-15 Last updated: 2010-03-01Bibliographically approved
5. Grey-level Morphology Based Segmentation of MRI of the Human Cortex
Open this publication in new window or tab >>Grey-level Morphology Based Segmentation of MRI of the Human Cortex
2001 (English)In: Proceedings of ICIAP’01 11th International Conference on Image Analysis and Processing, Palermo, Italy, 2001, p. 578–583-Chapter in book (Other academic)
Identifiers
urn:nbn:se:uu:diva-90812 (URN)
Available from: 2003-09-15 Created: 2003-09-15 Last updated: 2010-03-01Bibliographically approved
6. Segmentation of T1-MRI of the Human Cortex Using a 3D Grey-level Morphology Approach
Open this publication in new window or tab >>Segmentation of T1-MRI of the Human Cortex Using a 3D Grey-level Morphology Approach
2003 (English)In: Image Analysis: 13th Scandinavian Conference, SCIA2003 , Halmstad, Sweden, 2003, p. 462–469-Chapter in book (Other academic)
Identifiers
urn:nbn:se:uu:diva-90813 (URN)
Available from: 2003-09-15 Created: 2003-09-15 Last updated: 2010-03-01Bibliographically approved
7. Grey-level Morphology Combined with an Artificial Networks Approach for Multimodal Segmentation of the Hippocampus
Open this publication in new window or tab >>Grey-level Morphology Combined with an Artificial Networks Approach for Multimodal Segmentation of the Hippocampus
(English)In: Proceedings of ICIAP’03 12th International Conference on ImageChapter in book (Other academic)
Identifiers
urn:nbn:se:uu:diva-90814 (URN)
Available from: 2003-09-15 Created: 2003-09-15 Last updated: 2010-03-01Bibliographically approved
8. Image Analysis of Co-registered Autoradiographic Human Whole Hemisphere Sections
Open this publication in new window or tab >>Image Analysis of Co-registered Autoradiographic Human Whole Hemisphere Sections
Manuscript (Other academic)
Identifiers
urn:nbn:se:uu:diva-90815 (URN)
Available from: 2003-09-15 Created: 2003-09-15 Last updated: 2010-01-13Bibliographically approved
9. Segmentation of Multimodal MRI of Hippocampus Using 3D Greylevel Morphology Combined with Artificial Neural Networks
Open this publication in new window or tab >>Segmentation of Multimodal MRI of Hippocampus Using 3D Greylevel Morphology Combined with Artificial Neural Networks
Manuscript (Other academic)
Identifiers
urn:nbn:se:uu:diva-90816 (URN)
Available from: 2003-09-15 Created: 2003-09-15 Last updated: 2010-01-13Bibliographically approved

Open Access in DiVA

fulltext(1586 kB)5255 downloads
File information
File name FULLTEXT01.pdfFile size 1586 kBChecksum SHA-1
60471c2fb5012cda431bfb47f6682bbfd2921e75c33321d8de5b52ea3466f24a0e93f3f9
Type fulltextMimetype application/pdf
Buy this publication >>

By organisation
Centre for Image Analysis
Computer Vision and Robotics (Autonomous Systems)

Search outside of DiVA

GoogleGoogle Scholar
Total: 5255 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 2810 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf