Stage Zero: Pathology Detection

  All slices processed by the tumor segmentation system have been automatically classified as abnormal. They are known to contain glioblastoma-multiforme tumor based on radiologist pathology reports. Since this work is an extension of previous work, knowledge generated during ``pre-processing'' is available to the tumor segmentation system. Detailed information can be found in [21,22,23], but a brief summary is provided.

Slice processing begins by using an unsupervised fuzzy c-means (FCM) clustering algorithm [25,26] to segment the slice. The initial FCM segmentation is passed to an expert system which uses a combination of knowledge concerning cluster distribution in feature space and anatomical information to classify the slice as normal or abnormal. Two examples of knowledge (implemented as rules) used in the predecessor system are: (1) in a normal slice, CSF belongs to the cluster center with the highest T2 value in the intra-cranial region; (2) in image space, all normal tissues are roughly symmetrical along the vertical axis (defined by each tissue having approximately the same number of pixels in each brain hemisphere), while tumors often have poor symmetry. Abnormal slices are detected by their deviation from ``expectations'' concerning normal MR slices, such as the one shown in Figure 2 whose white matter class failed to completely enclose the ventricle area. An abnormal slice with the facts generated in labeling it abnormal are passed on to the tumor segmentation system. Normal slices have all pixels labeled.

  

Figure 3: Building the Intra-Cranial Mask. (a) The original FCM-segmented image; (b) pathology captured in Group 1 clusters; (c) intra-cranial mask using only Group 2 clusters; (d) mask after including Group 1 clusters with tumor; (e) mask after extra-cranial regions are removed.