Kluwer - Handbook of Biomedical Image Analysis Vol

Figure 9.25: Histogram of empirical data.

time step of 0.1 sec. Using this initialization decreases the number of iterations, leading to fast extraction of the vascular tree. The volume segmentation takes about 20 min. on the unix workstation with the super computer. Segmentation results are exposed to the connectivity filter to remove the nonvessel areas. Each volume is visualized to show the vascular tree. The segmentation accuracy was measured to be 94% which is very good for this type of data. The 2D phantom can be modified to be a 3D one simulating the whole volume leading to more accuracy. The results are promising with a good accuracy. This model can be extended to unsupervised case including a parameter estimation capability in future work. Future work will include geometrical features to the segmentation model to enhance the segmentation results.

Questions

1.What are the main three properties of MRF?

2.Using traditional EM algorithm, estimate the mean, the variance, and the proportional for the two classes shown in Fig. 9.25? (Hint: Before

applying EM algorithm, normalize f (y) such that f or all y f (y) = 1, and assume each class comes from normal distribution).

3.What are the main advantages of using the genetic algorithm as optimization tool?

4.When it is useful to use GMRF in image segmentation, and when is it not useful?

5.What is the advantages of using GMRF in image segmentation?

6.Derive the CFL restriction to find the optimal time step in 3D case.

7.Suggest an algorithm to mark the narrow band points in both 2D and 3D. Compare it with the use of the Dirac delta function.

8.Level sets are used to extract anatomical structures from 2D and 3D data. What are the advantages of using level sets in 3D?

9.Using the front as the zero level embedded in the surface has many advantages over using scattered points representing the front. What are these advantages?

10.If we have the front as a surface embedded in a 4D function, can we slice the front as curves in 2D to make the implementation easier? Why?

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