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Please use this identifier to cite or link to this item: http://hdl.handle.net/1807/31332

Title: Automatic Segmentation of Lung Carcinoma Using 3D Texture Features in Co-registered 18-FDG PET/CT Images
Authors: Markel, Daniel
Advisor: Caldwell, Curtis
Department: Medical Biophysics
Keywords: Segmentation
Machine Learning
Issue Date: 14-Dec-2011
Abstract: 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.
URI: http://hdl.handle.net/1807/31332
Appears in Collections:Master

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