Advanced Segmentation Techniques - Biomedical Image Analysis - page 490

Table 9.1: Estimated using dominant Gaussian components
extracting algorithm

Parameter

2
I 1

2
I 2

µ I 1 µ I 2 σ σ π I 1 π I 2

Value 59.29 139.97 177.15 344.29 0.25 0.758

dominant Gaussian components extracting algorithm described in 9.2.5.1. Figure
9.5 shows the empirical density for the CT slice shown in Fig. 9.4 and the initial
estimated density (which represented the two dominant Gaussian components
in the given CT). The Levy distance between the two distribution functions
which represented the densities shown in Fig. 9.5 is 0.09. This value is large and
this means there is a mismatch between empirical p em ( y ) and p I ( y ). Figure 9.6
shows the error and absolute error between p em ( y ) and p I ( y ).
After we apply sequential EM algorithm to | ζ ( y ) | , we get that the number of
normal components that represent | ζ ( y ) | is 10 as shown in Fig. 9.7. Figure 9.8

Figure 9.4: A typical slice form of a chest spiral CT scan.