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Permanent link (DOI): https://doi.org/10.7939/R3Q52FD8G

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Automatic Brain Tumor Segmentation Open Access

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Author or creator
Schmidt, Mark
Additional contributors
Subject/Keyword
magnetic resonance imaging
brain tumors
automatic segmentation
Type of item
Computing Science Technical Report
Computing science technical report ID
TR05-13
Language
English
Place
Time
Description
Technical report TR05-13. This thesis addresses the task of automatic segmentation of brain tumors and edema in Magnetic Resonance images. This is motivated by potential applications in assessing tumor growth, assessing treatment responses, computer-assisted surgery, radiation therapy target planning, and the construction of tumor growth models. The presented framework forms an image processing pipeline, consisting of noise reduction, spatial registration, intensity standardization, feature extraction, pixel classification, and label relaxation. The key advantage of this framework is the simultaneous use of features computed from the image intensity properties, and the locations of pixels within an aligned template brain in a standard coordinate system. Automatically learning to combine these features allows recognition of tumors and edema that have relatively normal intensity properties. Our results on 11 patients with brain tumors show that the system achieves nearly perfect performance given patient-specific training, but also achieves accurate results in segmenting patients not used in training.
Date created
2005
DOI
doi:10.7939/R3Q52FD8G
License information
Creative Commons Attribution 3.0 Unported
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