Biomedical Engineering Reference
In-Depth Information
100
90
FCM
EM
BCFM
80
70
60
50
40
30
20
10
0
0
70
40
50
60
80
90
10
20
30
Number of Iterations
Figure 9.16: Comparison of the performance of the proposed BCFCM algorithm
with EM and FCM segmentation when applied to the synthetic two-class image
shown in Fig. 9.15(a).
traditional FCM was unable to correctly classify the images. Both BCFCM and
EM segmented the image into three classes corresponding to background, gray
matter (GM), and white matter (WM). BCFCM produced slightly better results
than EM due to its ability to cope with noise. Moreover, BCFCM requires far
less number of iterations to converge compared to the EM algorithm. Table 9.2
depicts the segmentation accuracy (SA) of the three mentioned method when
applied to the MR phantom. SA was measured as follows:
Number of correctly classified pixels
Total number of pixels
SA
=
×
100%
(9.48)
SA was calculated for different SNR. From the results, we can see that the
three methods produced almost similar results for high SNR. BCFCM method,
however, was found to be more accurate for lower SNR.
