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Segmentation of Multimodal MRI of Hippocampus Using 3D Greylevel Morphology Combined with Artificial Neural Networks
Uppsala University, Interfaculty Units, Centre for Image Analysis.
Manuscript (Other academic)
URN: urn:nbn:se:uu:diva-90816OAI: oai:DiVA.org:uu-90816DiVA: diva2:163294
Available from: 2003-09-15 Created: 2003-09-15 Last updated: 2010-01-13Bibliographically 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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