maximize
[1] M. Droske, B. Meyer, M. Rumpf, and C. Schaller. An adaptive level set method for medical image segmentation. In R. Leahy and M. Insana, editors, Annual Symposium on Information Processing in Medical Imaging. Springer, Lecture Notes Computer Science, 2001.
bib | .pdf 1 ]
An efficient adaptive multigrid level set method for front propagation purposes in three dimensional medical image processing and segmentation is presented. It is able to deal with non sharp segment boundaries. A flexible, interactive modulation of the front speed depending on various boundary and regularization criteria ensure this goal. Efficiency is due to a graded underlying mesh implicitly defined via error or feature indicating values on the cells of the underlying hexahedral grid. A suitable saturation condition ensures an important regularity condition on the resulting adaptive grid. This simplifies the adaptive fast marching method on the compressed data significantly. As an application the segmentation of glioma is considered. Thus the clinician interactively selects a few parameters describing the speed function and a few seed points referring to a single slice of an MRI data set. Then the automatic process of front propagation generates a family of segments corresponding to the evolution of the front in time, from which the clinician finally selects an appropriate segment covered by the gliom. This selection can be based on a visual evaluation of the propagation on a reference slice using the clinicians expert knowledge. Thus, the overall glioma segmentation turns into an efficient, nearly real time process with intuitive and usefully restricted user interaction.