8 research outputs found
Foetal echocardiographic segmentation
Congenital heart disease affects just under one percentage of all live births [1].
Those defects that manifest themselves as changes to the cardiac chamber volumes
are the motivation for the research presented in this thesis.
Blood volume measurements in vivo require delineation of the cardiac chambers and
manual tracing of foetal cardiac chambers is very time consuming and operator
dependent. This thesis presents a multi region based level set snake deformable
model applied in both 2D and 3D which can automatically adapt to some extent
towards ultrasound noise such as attenuation, speckle and partial occlusion artefacts.
The algorithm presented is named Mumford Shah Sarti Collision Detection (MSSCD).
The level set methods presented in this thesis have an optional shape prior term for
constraining the segmentation by a template registered to the image in the presence
of shadowing and heavy noise.
When applied to real data in the absence of the template the MSSCD algorithm is
initialised from seed primitives placed at the centre of each cardiac chamber. The
voxel statistics inside the chamber is determined before evolution. The MSSCD stops
at open boundaries between two chambers as the two approaching level set fronts
meet. This has significance when determining volumes for all cardiac compartments
since cardiac indices assume that each chamber is treated in isolation. Comparison
of the segmentation results from the implemented snakes including a previous level
set method in the foetal cardiac literature show that in both 2D and 3D on both real
and synthetic data, the MSSCD formulation is better suited to these types of data.
All the algorithms tested in this thesis are within 2mm error to manually traced
segmentation of the foetal cardiac datasets. This corresponds to less than 10% of
the length of a foetal heart. In addition to comparison with manual tracings all the
amorphous deformable model segmentations in this thesis are validated using a
physical phantom. The volume estimation of the phantom by the MSSCD
segmentation is to within 13% of the physically determined volume
Model-driven segmentation of X-ray left ventricular angiograms
X-ray left ventricular (LV) angiography is an important imaging modality to assess cardiac function. Using a contrast fluid a 2D projection of the heart is obtained. In current clinical practice cardiac function is analyzed by drawing two contours manually: one in the end diastolic (ED) phase and one in the end systolic (ES) phase. From the contours the LV volumes in these phases are calculated and the patient__s ejection fraction is assessed. Drawing these contours manually is a cumbersome and time-consuming task for a medical doctor. Furthermore, manual drawing introduces inter- and intra-observer variabilities. The focus of the research presented in this thesis was to automate the process of contour drawing in X-ray LV angiography. The developed method is based on Active Appearance Models. These statistical models, in which the cardiac shape and the cardiac appearance are modeled, have proven to be able to mimic the drawing behavior of an expert cardiologist. The clinical parameters, as determined by the automated method, showed a similar degree of accuracy as when determined by an expert. Furthermore, the required time for patient analysis was reduced considerably and the inter- and intra-observer variabilities were structurally decreased.UBL - phd migration 201
Characterising pattern asymmetry in pigmented skin lesions
Abstract. In clinical diagnosis of pigmented skin lesions asymmetric pigmentation is often indicative of
melanoma. This paper describes a method and measures for characterizing lesion symmetry. The estimate of
mirror symmetry is computed first for a number of axes at different degrees of rotation with respect to the
lesion centre. The statistics of these estimates are the used to assess the overall symmetry. The method is
applied to three different lesion representations showing the overall pigmentation, the pigmentation pattern,
and the pattern of dermal melanin. The best measure is a 100% sensitive and 96% specific indicator of
melanoma on a test set of 33 lesions, with a separate training set consisting of 66 lesions
Quantitative Analysis of Ultrasound Images of the Preterm Brain
In this PhD new algorithms are proposed to better understand and diagnose white matter damage in the preterm Brain. Since Ultrasound imaging is the most suited modality for the inspection of brain pathologies in very low birth weight infants we propose multiple techniques to assist in what is called Computer-Aided Diagnosis. As a main result we are able to increase the qualitative diagnosis from a 70% detectability to a 98% quantitative detectability