Kluwer - Handbook of Biomedical Image Analysis Vol

4.3.3.2 Registration of Stereo Images

Visual inspection of the registration results reveals that mutual information maximization registration succeeded 9 times out of 11 and it failed on ll5/lr5 of set 1 and ll7/lr7 of set 3. The success rate is 82%. The average registration time is 11.0 seconds with a standard deviation of 1.5, which amounts to a speedup of 8.39. Again, the speedup value is only indicative.

For the stereo registration, the mean and standard deviation of misregistration parameters are (−0.46 ± 1.69, −0.67 ± 1.72, −0.02 ± 0.24, 0.0043 ± 0.0082). The estimation of the rotation angle and the scaling factors are very accurate.

The RMSE numbers are (1.75, 1.85, 0.25, 0.0090). The translation RMSE numbers are larger than what Ritter et al. reported, but the rotation and scaling are comparable to their results. As we mentioned earlier, it is hard to pair the points in two stereo images.

4.3.3.3 Registration for Temporal and Stereo Images

If we combine all results discussed above, then the success rate is 86%. The average time for registration is 11.7 seconds with a standard deviation of 2.6, which is about 7.83 speedup against Ritter et al. [5].

The average misregistrations are (−0.32, −0.17, −0.01, 0.0021) for x and y translations, rotation angle, and scaling factor, respectively. The RMSE are (1.49, 1.39, 0.21, 0.0070).

4.4 Summary

Retinal image registration and fusion is an important tool in ophthalmology for diagnosis, lesion progression assessment, and treatment monitoring. Registration by mutual information maximization is one of the most popular automatic algorithms. We implemented such a software system for automatic retinal image registration by mutual information maximization. The software was written in Java and Java 2D for maximum portability. The robust software architecture (MVC framework) was employed to ensure software maintainability and extensibility. The simplex downhill method was exploited to optimize the mutual information function as a function of the registration parameters. To increase the search range and to avoid being trapped by local optimal, multiresolution

Questions

1.What is image registration?

2.How is the image registration used in retinal imaging?

3.What are the characteristics of retinal image registration?

4.What is mutual information?

5.What is mutual information based registration?

6.What is image interpolation? Why is it important in image registration process?

7.Why is the exhaustive optimization not feasible in the mutual information registration process?

8.What is a multiresolution optimization? Why is it needed? How is it applied in the registration process?

9.Why is the mutual information registration algorithm written in Java?

10.What is the MVC framework?

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5.1 Introduction

5.1.1 Brain Aneurysms

Brain aneurysms are pathological dilatations of cerebral arteries. These dilatations consist in a progressive enlargement and deformation of the vessel wall produced by blood flow pressure. Brain aneurysms tend to occur at or near arterial bifurcations, mostly at the Circle of Willis, the vascular system that irrigates the basis of the brain.

The Circle of Willis is made up of several vascular segments (Fig. 5.1). The precommunicating segments (A1, A2) of the left and right Anterior Cerebral Arteries (ACA) and the Anterior Communicating Artery (ACoA) form the anterior part of the circle. The postcommunicating segments (P1, P2) of the left and right Posterior Cerebral Arteries (PCA) with the Posterior Communicating Arteries (PCoA) form the posterior part of the circle. The left and right PCoAs emerge from the left and right Internal Carotid Arteries (ICA). The Basilar Artery (BA) and the Middle Cerebral Arteries (MCA), complete the description of the Circle of Willis.

Brain aneurysms are classified into saccular and non-saccular types according to their shape (Fig. 5.2). Non-saccular aneurysms include atherosclerotic, fusiform, traumatic, and mycotic types. Saccular, or berry, aneurysms typically arise at a bifurcation or along turns of the parent vessel, or they point in the direction in which the blood flow would proceed if the turn were not present. Brain aneurysms are named according to the artery or segment of origin. For example, anterior communicating aneurysms are located at the ACoA, and

Figure 5.1: Three-dimensional model of the Circle of Willis. Image courtesy of Dr. Juan R. Cebral, George Mason University. (Color Slide)

posterior communicating aneurysms are usually located at the ICA, near the origin of the PCoA.

The prevalence of unruptured cerebral aneurysms is unknown, but it is estimated to be as high as 5% of the population [1]. The most serious complication of a brain aneurysm is its rupture and the consequent aneurysmal subarachnoid hemorrhage (SAH) with an incidence of sudden death of the 12.4% and rates

(a) (b)

Figure 5.2: Models of aneurysm. (a) Saccular aneurysm. (b) Non-saccular (fusiform) aneurysm.