Abstract
Pulmonary embolism (PE) is the sudden obstruction of an artery in the lungs,
usually due to a blood clot. There are more than 50 cases of PE per 100,000
persons every year in the USA. Of these cases, 11% die in the first hour and in
total, the untreated mortality rate of PE is estimated to be 30%. Thus, PE is a
common disorder with a high morbidity and mortality for which an early and precise
diagnosis is highly desirable.
Contrast-enhanced multi-slice x-ray computed tomography (CT) has become the
preferred initial imaging test (and often the only test) to diagnose PE, because it is
a simple, minimally invasive, fast and high-resolution imaging technique that allows
the direct depiction of a clot inside the blood vessels. The CT image can also be
used to identify other potentially life-threatening causes in a patient with chest pain.
In contrast-enhanced CT (i.e., CT angiography, CTA) images, the blood vessels
appear to be very bright because the contrast material is dissolved in blood. The
embolus does not absorb contrast material, and therefore, it remains darker. PE can
be recognized in CTA images as a dark area in the pulmonary arteries. However,
manual detection of the dark spots that correspond to PE in CT images is often
described by radiologists as difficult and time consuming. Therefore, computer-aided
diagnosis (CAD) is desirable.
In this thesis, we propose a new CAD system for automatic detection of PE
in CTA images. The evaluation shows that the performance of our system is at
the level of state of the art literature. The data was selected to demonstrate a
variety of thrombus load, considerable breathing artifacts, sub-optimal contrast and
parenchymal diseases, and none of the emboli were excluded for evaluation. This
is important because the main problem of PE detection is the separation between
true PE and look-alikes, which is much harder when the patient is not healthy.
The CAD system that we propose consists of several steps. In the first step,
pulmonary vessels are segmented and PE candidates are detected inside the vessel
segmentation. The candidate detection step focusses on the inclusion of PE –
even when vessels are completely occluded – and the exclusion of false detections,
such as lymphoid tissue and parenchymal diseases. Subsequently, features are
computed on each of the candidates to enable classification of the candidates. The
feature-computation step does not only focus on the intensity, shape and size of
an embolus, but also on relative locations and the regular shape of the pulmonary
vascular tree. In the last step, classification is used to separate candidates that
represent real emboli from the other candidates. The system is optimized with
feature selection and classifier selection. Several classifiers have been tested and
the results show that the performance is optimized by using a bagged tree classifier
with the features distance-to-parenchyma and stringness. The system was trained
on 38 CT data sets. Evaluation on 19 other data sets showed that the system
generalizes well. The sensitivity of our system on the evaluation data is 63% at 4.9
false positives per data set, which allowed the radiologist to improve the number of
detected PE with 22%.
Another part of this thesis is about the accurate quantification of the vessel diameter
in CT images. Quantification of the local vessel diameter is essential for the correct
diagnosis of vascular diseases. For example, the relative decrease in diameter of
a stenosis is an important factor in determining the treatment therapy. However,
inherent to image acquisition is a blurring effect, which causes a bias in the diameter
estimation of most methods. In this thesis, we focus on fast and accurate (unbiased)
vessel-diameter quantification.
For the localization of the vessel wall, Gaussian derivatives are often used as differential
operators. We show how these Gaussian derivatives should be computed on
multi-dimensional data with anisotropic voxels and anisotropic blurring. The voxels
and blurring are usually anisotropic in the 3D CT images, which means that the
voxel size and the amount of blur is not equal in all three directions. Furthermore,
we show that the computational cost of interpolation and differentiation on Gaussian
blurred images can be reduced by using B-spline interpolation and approximation,
without losing accuracy. We introduce a derivative-based edge detector with unbiased
localization on curved surfaces in spite of the blur in CT images. Finally,
we propose a modification of the full-width at half-maximum (FWHM) criterion to
create an unbiased method for vessel-diameter quantification in CT images. This
criterion is not only cheaper but also more robust to noise than the commonly used
derivative-based edge detectors.
Original language | English |
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Qualification | Doctor of Philosophy |
Awarding Institution |
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Supervisors/Advisors |
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Award date | 2 Apr 2008 |
Place of Publication | Eindhoven |
Publisher | |
Print ISBNs | 978-90-386-1209-6 |
DOIs | |
Publication status | Published - 2008 |