A strength of the knowledge-based (KB) systems in [21,22,23] has been their ``coarse-to-fine'' operation. Instead of attempting to achieve their task in one step, incremental refinement is applied with easily identifiable tissues located and labeled first. Removing labeled pixels from further consideration allows a ``focus'' to be placed on the remaining (fewer) pixels, where more subtle trends may become clearer. The tumor segmentation system is similarly designed. To better illustrate the system's organization, we present it at a conceptual level. Figure 2 shows the primary steps in extracting tumor from raw MR data. Section 3 described these steps in more detail.
The system has five primary steps. First a pre-processing stage developed in previous works [21,22,23], called Stage Zero here, is used to detect deviations from expected properties within the slice. Slices that are free of abnormalities are not processed further. Otherwise, Stage One extracts the intra-cranial region from the rest of the MR image based on information provided by pre-processing. This creates an image mask of the brain that limits processing in Stage Two to only those pixels contained by the mask. In fact, a particular Stage operates only on the foreground pixels that are contained in a mask produced by the completion of the previous Stage.
An initial tumor segmentation is produced in Stage Two through a combination of adaptive histogram thresholds in the T1 and PD feature images. The initial tumor segmentation is passed on to Stage Three, where additional non-tumor pixels are removed via a ``density screening'' operation. Density screening is based on the observation that pixels of normal tissues are grouped more closely together in feature space than tumor pixels.
Stage Four completes tumor segmentation by analyzing each spatially disjoint ``region'' in image space separately. Regions found to be free of tumor are removed, with those regions remaining labeled as tumor. The resulting image is considered the final tumor segmentation and can be compared with a ground truth image.
