General finite element model for segmentation in 2, 3, and 4 dimensions

Joerg Bredno; Thomas Martin Lehmann; Klaus Spitzer

doi:10.1117/12.387623

6 June 2000 General finite element model for segmentation in 2, 3, and 4 dimensions

Joerg Bredno, Thomas Martin Lehmann, Klaus Spitzer

Proceedings Volume 3979, Medical Imaging 2000: Image Processing; (2000) https://doi.org/10.1117/12.387623
Event: Medical Imaging 2000, 2000, San Diego, CA, United States

Abstract

Medical imaging modalities often provide image material in more than two dimensions. However, the analysis of voxel data sets or image sequences is usually performed using only two- dimensional methods. Furthermore, four-dimensional medical image material (sequences of stacks of images) is available already for clinical diagnoses. Contrarily, four-dimensional image processing methods are almost unknown. We present an active contour model based on balloon models that allows a coherent segmentation of image material of any desired dimension. Our model is based on linear finite elements and combines a shape representation with an iterative segmentation algorithm. Additionally, we present a novel definition for the computation of external influences to deform the model. The appearance of relevant edges in the image is defined by image potentials and a filter kernel function. The filter kernel is applied with respect to the location and orientation of finite elements. The model moves under the influence of internal and external forces and avoids collisions of finite elements in this movement. Exemplarily, we present segmentation results in 2D (radiographs), 3D (video sequence of the mouth), and 4D (synthetic image material) and compare our results with propagation methods. The new formalism for external influences allows the model to act on graylevel as well as color images without pre-filtering.

Citation Download Citation

Joerg Bredno, Thomas Martin Lehmann, and Klaus Spitzer "General finite element model for segmentation in 2, 3, and 4 dimensions", Proc. SPIE 3979, Medical Imaging 2000: Image Processing, (6 June 2000); https://doi.org/10.1117/12.387623

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