Universiẗat, Diss., 2004--Mannheim.

This thesis tries to answer if a semi-automatic tool can speed up the process of segmenting tumors to find the area of a slice in the tumor or the volume of the entire tumor. A few different 2D semi-automatic tools were considered. The final choice was to implement live-wire. The implemented live-wire was evaluated and improved upon with hands-on testing from developers. Two methods were found for extending live-wire to 3D bodies. The first method was to interpolate the seed points and create new contours using the new seed points. The second method was to let the user segment contours in two orthogonal projections. The intersections between those contours and planes in the third orthogonal projection were then used to create automatic contours in this third projection. Both tools were implemented and evaluated. The evaluation compared the two tools to manual segmentation on two cases posing different difficulties. Time-on-task and accuracy were measured during the evaluation. The evaluation revealed that the semi-automatic tools could indeed save the user time while maintaining acceptable (80%) accuracy. The significance of all results were analyzed using two-tailed t-tests.

The subject of this thesis is a method proposed for automated evaluation of the parameters of X-ray of cystic disorders in human jawbones. The main problem in medical diagnostic is the low repeatability due to the subjective evaluation of images without using a tool for image processing. In this thesis are described the basic steps of image processing, various methods of image segmentation and chosen segmentation method live-wire. Selected segments were processed in the ImageJ Java environment. In the cystic regions their basic statistical and shape properties were evaluated. The obtained values were used for learning the classification model (decision tree) in the environment RapidMiner. This model was used to create a plug-in for automatic classification of the type of cysts in the program ImageJ.

Maternal diet can have a great impact on the health and development of the fetus. Poor fetal nutrition has been linked to the development of a set of conditions in later life, such as coronary heart disease, type 2 diabetes and hypertension, while restricted growth can result in hypogylcemia, hypocalcemia, hypothermia, polycythemia, hyperbilirubinemia and cerebral palsy. High alcohol consumption during pregnancy can result in Fetal Alcohol Syndrome, a condition that can cause growth retardation, lowered intelligence and craniofacial defects. Current biometric assessment of the fetus involves size-based measures which may not accurately portray the state of fetal development, since they cannot differentiate cases of small-but-healthy or large-but-unhealthy fetuses. This thesis aims to outline a set of more appropriate measures of accurately capturing the state of fetal development. Specifically, soft tissue area and liver volume measurement are examined, followed by facial shape characterisation. A number of tools are presented which aim to allow clinicians to achieve accurate segmentations of these landmark regions. These are modifications on the Live Wire algorithm, an interactive segmentation method in which the user places a number of anchor points and a minimum cost path is calculated between the previous anchor point and the cursor. This focuses on giving the clinician intuitive control over the exact position of the segmented contour. These modifications are FA-S Live Wire, which utilises Feature Asymmetry and a weak shape constraint, ASP Live Wire, which is a 3D expansion of Live Wire, and FA-O Live Wire, which uses Feature Asymmtery and Local Orientation to guide the segmentation process. These have been designed with each of the specific biometric landmarks in mind. Finally, a method of characterising fetal face shape is proposed, using a combination of the segmentation methods described here and a simple shape model with a parameterised b-spline meshing approach to facial surface representation.