Knowledge Based Automatic Segmentation

Even the most sophisticated pre- and postprocessing techniques cannot, however, overcome the inherent limitation of the basically intensity-based methods, namely the assumption that segmentation can be carried out solely based on information provided by the actual image. This assumption is fundamentally

Figure 14.3: Brain segmentation based on morphological postprocessing. Image (a) shows the result of thresholding, which has been eroded (b) in order to break up unwanted connections between different organs. Brain tissue has been identified by connected component labeling (c) and has been dilated back to its original extent (d).

wrong, and the radiologist uses a broad range of related knowledge on the field of anatomy, pathology, physiology, and radiology in order to arrive at a reasonable image interpretation. The incorporation of such knowledge into the algorithms used is therefore indispensable for automatic image segmentation.

Different procedures have been proposed in the literature to approach the problem of representation and usage of prior knowledge for image analysis. Because of the enormous complexity of the necessary prior information, classical methods of artificial intelligence as the use of expert systems [12,13] can offer only limited support to solve this problem.

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