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Grey-level Morphology Combined with an Artificial Networks Approach for Multimodal Segmentation of the Hippocampus
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Centrum för bildanalys. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datoriserad bildanalys.
(engelsk)Inngår i: Proceedings of ICIAP’03 12th International Conference on ImageKapittel i bok, del av antologi (Annet vitenskapelig)
Identifikatorer
URN: urn:nbn:se:uu:diva-90814OAI: oai:DiVA.org:uu-90814DiVA, id: diva2:163292
Tilgjengelig fra: 2003-09-15 Laget: 2003-09-15 Sist oppdatert: 2010-03-01bibliografisk kontrollert
Inngår i avhandling
1. Segmentation and Visualisation of Human Brain Structures
Åpne denne publikasjonen i ny fane eller vindu >>Segmentation and Visualisation of Human Brain Structures
2003 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Uppsala: Acta Universitatis Upsaliensis, 2003. s. 68
Serie
Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1104-232X ; 885
Emneord
Bildanalys, segmentation, visualisation, brain, cortex, hippocampus, autoradiography, MRI, PET, ROI, Bildanalys
HSV kategori
Forskningsprogram
datoriserad bildanalys
Identifikatorer
urn:nbn:se:uu:diva-3567 (URN)91-554-5729-0 (ISBN)
Disputas
2003-10-10, X, Universitetshuset, Uppsala, 10:15
Opponent
Veileder
Tilgjengelig fra: 2003-09-15 Laget: 2003-09-15 Sist oppdatert: 2018-01-13bibliografisk kontrollert

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