[Insight-users] Image Registration / Convergence of optimizer

[Koning, P.J.H. de (LKEB)]

Hello,

I am trying to register two 3D brain images. I am using the MutualInformationImageToImageMetric combined with a VersorRigid3DTransform and the VersorRigid3DTransformOptimizer. I am getting some good results, but when I look at the GetValue() of the optimizer during the iterations, I can't see it convergence (although the parameters of the rigid transform do show a convergence).

Is this value not supposed to converge to a maximum value (I set MaximizeOn() for the optimzer)?

Secondly, when I look at the values of the optimizer after the optimization has finished, I see that the current iteration has increased by 2, the transfor parameters stay constant and the GetValue() of the optimizer has changed ?!? How did this happen?

A hope you can shed some light on this (for me) strange behaviour.


Patrick de Koning




P.S. Below you can see the output of the optimizer at each iteration:

(Iteration number = optimizer value : [transform])
0 = 0.119745 : 	[-1.38545e-005 -2.53032e-006 ,7.4684e-006 ,-0.186788 ,0.179258 ,4.99329
1 = 0.0503912 : 	[-1.71608e-005, -8.88087e-007, 9.23982e-006, -0.191543, 0.331362, 2.49793]
2 = 0.55825 : 	[-1.74154e-005, -3.9016e-007, 9.85893e-006, -0.204029, 0.326522, 3.74786]
3 = 0.395624 : 	[-1.77931e-005, 1.00222e-006, 1.00231e-005, -0.193226, 0.306919, 3.12326]
4 = 0.3617 : 	[-1.71888e-005, -1.22213e-006, 7.93444e-006, -0.200906, 0.316677, 2.49838]
5 = 0.335809 : 	[-1.81236e-005, -2.72138e-006, 6.68041e-006, -0.16859, 0.298884, 2.8087]
6 = 0.414256 : 	[-1.81705e-005, -2.66581e-006, 7.14423e-006, -0.176486, 0.29259, 2.65277]
7 = -0.0230083 : 	[-1.8131e-005, -2.49584e-006, 6.81443e-006, -0.179083, 0.28925, 2.49658]
8 = 0.328098 : 	[-1.82165e-005, -2.45698e-006, 7.11142e-006, -0.174855, 0.283865, 2.5744]
9 = -0.0404819 : 	[-1.81304e-005, -2.5165e-006, 7.35304e-006, -0.177024, 0.286958, 2.53553]
10 = 0.0277824 : 	[-1.81428e-005, -2.51756e-006, 7.50534e-006, -0.175715, 0.28891, 2.49653]
11 = 0.241717 : 	[-1.81675e-005, -2.4732e-006, 7.59313e-006, -0.174454, 0.290408, 2.45752]
12 = 0.198569 : 	[-1.81956e-005, -2.50076e-006, 7.62124e-006, -0.174316, 0.289997, 2.47705]
13 = 0.211859 : 	[-1.81711e-005, -2.48456e-006, 7.67883e-006, -0.175431, 0.28911, 2.46739]
14 = 0.271563 : 	[-1.81712e-005, -2.50097e-006, 7.66833e-006, -0.175269, 0.288967, 2.45762]
15 = 0.22593 : 	[-1.81791e-005, -2.48652e-006, 7.63361e-006, -0.175413, 0.288978, 2.44786]
16 = 0.954839 : 	[-1.81924e-005, -2.48619e-006, 7.62184e-006, -0.175887, 0.289044, 2.4381]
17 = 0.245999 : 	[-1.81852e-005, -2.46701e-006, 7.60605e-006, -0.175916, 0.289297, 2.44298]
18 = 0.163771 : 	[-1.81861e-005, -2.46174e-006, 7.60433e-006, -0.175958, 0.289195, 2.44054]
19 = 0.472102 : 	[-1.82019e-005, -2.47041e-006, 7.59436e-006, -0.175931, 0.289398, 2.43811]
20 = 0.306671 : 	[-1.8204e-005, -2.47435e-006, 7.58475e-006, -0.175902, 0.289338, 2.43567]
21 = 0.362819 : 	[-1.8203e-005, -2.47463e-006, 7.5833e-006, -0.175974, 0.289261, 2.43688]
22 = 0.33775 : 	[-1.82027e-005, -2.47465e-006, 7.58338e-006, -0.175984, 0.289256, 2.43627]
23 = 0.062375 : 	[-1.82026e-005, -2.47405e-006, 7.58279e-006, -0.175974, 0.289248, 2.43566]
24 = 0.294642 : 	[-1.82027e-005, -2.47339e-006, 7.58462e-006, -0.17596, 0.289238, 2.43505]
25 = 0.375321 : 	[-1.82031e-005, -2.47345e-006, 7.58648e-006, -0.175964, 0.289261, 2.43536]
26 = 0.295915 : 	[-1.82031e-005, -2.47429e-006, 7.58632e-006, -0.175959, 0.289254, 2.43521]
27 = 0.488161 : 	[-1.82028e-005, -2.47451e-006, 7.58712e-006, -0.175985, 0.289253, 2.43506]

[End of optimization]

29 = 0.366311 : 	[-1.82028e-005, -2.47451e-006, 7.58712e-006, -0.175985, 0.289253, 2.43506]