Knowledge is any chunk of information that effectively discriminates one class type from another [28]. In this case, tumor will have certain properties that other brain tissues will not and visa-versa. In the domain of MRI volumes, there are two primary sources of knowledge available. The first is pixel intensity in feature space, which describes tissue characteristics within the MR imaging system, which are summarized in Table 2 (based on a review of literature [33,34,35]). The second is image/anatomical space and includes expected shapes and placements of certain tissues within the MR image, such as the fact that CSF lies within the ventricles, as shown in Figure 1(a). Our previous efforts in [21,22,23] exploited both feature-domain and anatomical knowledge, using one source to verify decisions based on the other source. The nature of tumors limits the use of anatomical knowledge, since they can have any shape and occupy any area within the brain. As a result, knowledge contained in feature space must be extracted and utilized in a number of novel ways. As each processing stage is described in Section 3, the specific knowledge extracted and its application will be detailed.
| Pulse Sequence | Effect | Tissues |
| (TR/TE) | (Signal Intensity) | |
| T1-weighted | Short T1 relaxation | Fat, Lipid-Containing Molecules, |
| (short/short) | (bright) | Proteinaceous Fluid, Paramagnetic |
| Substances (Gadolinium) | ||
| Long T1 relaxation | Neoplasm, Edema, CSF, | |
| (dark) | Pure Fluid, Inflammation | |
| PD-weighted | High proton density | Fat, Fluids |
| (long/short) | (bright) | |
| Low proton density | Calcium, Air, | |
| (dark) | Fibrous Tissue, Cortical Bone | |
| T2-weighted | Short T2 relaxation | Iron containing substances |
| (long/long) | (dark) | (blood-breakdown products) |
| Long T2 relaxation | Neoplasm, Edema, CSF, | |
| (bright) | Pure Fluid, Inflammation |