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Lung segmentation is an important first step for quantitative lung CT image analysis and computer aided diagnosis. However, accurate and automated lung CT image segmentation may be made difficult by the presence of the abnormalities. Since many lung diseases change tissue density, resulting in intensity changes in the CT image data, intensity-only segmentation algorithms will not work for most pathological...
Statistical shape and intensity modelling have been subject to an increasing interest within the past decade. However, construction of such models requires large number of segmented examples. Accurate and automatic segmentation techniques that do not require any explicit prior model are therefore of high interest. We propose a fully-automatic method for segmenting the femur in 3D Computed Tomography...
Computed tomography (CT) is used for the attenuation correction of positron emission tomography (PET) to enhance the efficiency of data acquisition process and to improve the quality of the reconstructed PET data in the brain. Due to the use of two different modalities, chances of misalignment between PET and CT images are quite significant. The main cause of this misregistration is the motion of...
This work presents three different methods for automatic detection of anatomical landmarks in CT data, namely for the left and right anterior superior iliac spines and the pubic symphysis. The methods exhibit different degrees of generality in terms of portability to other anatomical landmarks and require a different amount of training data. The first method is problem-specific and is based on the...
In this paper problem of airway tree segmentation from CT data sets was regarded. The idea of segmentation using 3D seeded region growing algorithm was introduced. Common problems arising during extracting airway tree from CT data using region growing method were explained. Especially problem of leakage from airway into the lung parenchyma was outlined. Common methods of CT data sets preprocessing...
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