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GPU-Based Airway Tree Segmentation and Centerline Extraction
Norwegian University of Science and Technology, Faculty of Information Technology, Mathematics and Electrical Engineering, Department of Computer and Information Science.
2012 (English)MasteroppgaveStudent thesis
Abstract [en]

Lung cancer is one of the deadliest and most common types of cancer in Norway. Early and precise diagnosis is crucial for improving the survival rate. Diagnosis is often done by extracting a tissue sample in the lung through the mouth and throat. It is difficult to navigate to the tissue because of the complexity of the airways inside the lung and the reduced visibility. Our goal is to make a program that can automatically extract a map of the Airways directly from X-ray Computer Tomography(CT) images of the patient. This is a complex task and requires time consuming processing. In this thesis we explore different methods for extracting the Airways from CT images. We also investigate parallel processing and the usage of modern graphic processing units for speeding up the computations. We rate several methods in terms of reported performance and the possibility of parallel processing. The best rated method is implemented in a parallel framework called Open Computing Language. The results shows that our implementation is able to extract large parts of the Airway Tree, but struggles with the smaller airways and airways that deviate from a perfect circular cross-section. Our implementation is able to process a full CT scan using less than a minute with a modern graphic processing units. The implementation is very general and is able to extract other tubular structures as well. To show this we also run our implementation on a Magnetic Resonance Angio dataset for finding blood vessels in the brain and achieve good results. We see a lot of potential in this method for extracting tubular structures. The method struggles the most with noise handling and tubes that deviate from a circular cross-sectional shape. We believe that this can be improved by using another method than ridge traversal for the centerline extraction step. Because this is a local greedy algorithm, it often terminates prematurely due to noise and other image artifacts.

Place, publisher, year, edition, pages
Institutt for datateknikk og informasjonsvitenskap , 2012. , 101 p.
Keyword [no]
ntnudaim:6751, MTDT datateknikk, Intelligente systemer
URN: urn:nbn:no:ntnu:diva-18353Local ID: ntnudaim:6751OAI: diva2:565858
Available from: 2012-11-08 Created: 2012-11-08

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