Segmentation of Medical Image Volumes

Segmentation is a process that separates objects in an image. In medical images, particularly image volumes, the field of application is wide. For example 3D visualisations of the anatomy could benefit enormously from segmentation. The aim of this thesis is to construct a segmentation tool. The project consist three main parts. First, a survey of the actual need of segmentation in medical image volumes was carried out. Then a unique three-step model for a segmentation tool was implemented, tested and evaluated. The first step of the segmentation tool is a seed-growing method that uses the intensity and an orientation tensor estimate to decide which voxels that are part of the project. The second step uses an active contour, a deformable “balloon”. The contour is shrunk to fit the segmented border from the first step, yielding a surface suitable for visualisation. The last step consists of letting the contour reshape according to the orientation tensor estimate. The use evaluation establishes the usefulness of the tool. The model is flexible and well adapted to the users’ requests. For unclear objects the segmentation may fail, but the cause is mostly poor image quality. Even though much work remains to be done on the second and third part of the tool, the results are most promising