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Automatic Segmentation of Lung Carcinoma Using 3D Texture Features in Co-registered 18-FDG PET/CT Images

Variability between oncologists in defining the tumor during radiation therapy planning
can be as high as 700% by volume. Robust, automated definition of tumor boundaries
has the ability to significantly improve treatment accuracy and efficiency. However, the information provided in computed tomography (CT) is not sensitive enough to differences between tumor and healthy tissue and positron emission tomography (PET) is hampered by blurriness and low resolution. The textural characteristics of thoracic tissue was investigated and compared with those of tumors found within 21 patient PET and CT images in order to enhance the differences and the boundary between cancerous and healthy tissue. A pattern recognition approach was used from these samples to learn the textural characteristics of each and classify voxels as being either normal or abnormal.
The approach was compared to a number of alternative methods and found to have the
highest overlap with that of an oncologist's tumor definition.

Identiferoai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/31332
Date14 December 2011
CreatorsMarkel, Daniel
ContributorsCaldwell, Curtis
Source SetsUniversity of Toronto
Languageen_ca
Detected LanguageEnglish
TypeThesis

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