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Combined Whole Brain Segmentation and DT-MRI Using 3D Slicer
KTH, School of Technology and Health (STH), Medical Engineering.ORCID iD: 0000-0002-2426-8170
2013 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The development of various high-end medical imaging devices and modalities besides the continuous progress of the computational and processing power and the huge storage and distribution capabilities has made the medical image processing and quantitative analysis an inevitable challenge. In the past, medical experts used to perform manual comparison of the different images, but the process was time consuming and in many cases incapable of giving useful information. For performing image analysis and acquiring useful information from the different types of medical imaging data, several image processing techniques should be applied to the acquired datasets. In the field of human brain image analysis, the most common image processing techniques are segmentation, registration, 3D model making, 3D visualization and the diffusion tensor image analysis and visualization tools such as tractography.

Brain imaging quantitative analysis enables less invasive surgeries in image guided therapy, better diagnosis of neurological disorders and makes the comparison in multiple-subjects studies feasible. The goal of this project is to provide a human brain quantitative analysis pipeline to combine an array of different image modalities: T1-weighted MRI, T2-weighted MRI and diffusion weighted MRI. By using the different tools and capabilities of the open source software 3D Slicer, the different image modalities have registered together, a combined T1-weighted and T2 weighted segmentation performed to label the three main classes of intracranial tissue. The brain parcellation algorithm was used to extract the anatomical sub-structures of brain gray matter and whiter matter. Diffusion tensor MRI volume calculated from the DWI datasets and a whole brain tractography was performed. Finally, all the results including different label maps and 3D models was presented in a single combined framework. The presented pipeline can be used in future studies for brain medical image quantitative analysis.

Place, publisher, year, edition, pages
2013.
Series
TRITA-STH ; 2013:3
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:kth:diva-122314OAI: oai:DiVA.org:kth-122314DiVA, id: diva2:622009
Subject / course
Medical Imaging
Educational program
Master of Science - Medical Engineering
Supervisors
Examiners
Available from: 2019-09-11 Created: 2013-05-20 Last updated: 2019-09-11Bibliographically approved

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CiteExportLink to record
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