Conclusion

In this section, we investigated the segmentation algorithm based on multiple dimensional MRF model and clustering-based solutions, and introduced an effective approach for MCW MR image segmentation. This technique is based on mean-shift density estimation algorithm and was carefully designed to overcome the drawbacks in other existing methods. Experimental comparisons with histology section have demonstrated its successful performance.

For the processing speed of the proposed DMC-based approach, the same 50 multiple contrast weighing MR images with different image size were also

Table 8.6: Average segmentation time of DMC and mMRF

Image size

DMC (sec)

mMRF (sec)

128 x 128

8.608

92.104

256 x 256

19.140

244.328

512 x 512

27.937

517.163

used for testing. The comparison of the average segmentation time for DMC and mMRF approaches are shown in Table 8.6 which indicates that DMC uses much less time than mMRF.

In the validation experiments with histology sections, we can also note that poor image quality can reduce the accuracy of the proposed method by reading the cases that showed little agreement with histology. One hypothesis of this problem is that the poor separation of data in the vector space V makes the segmentation method unable to distinguish the different clusters.

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