[Insight-users] Re: itkmattesMutualInformationImagetoImageMetric
Grace Chen
Grace.Chen at swri.ca
Wed May 31 11:09:32 EDT 2006
Thanx for your inputs!
However, because I use image pyramid in my registration. I prints the metric
value out during each interation. I get increased value everytime the
program move to another level. (The print-out of the program is included
below.)
Does that mean image pyramid is not good for multi-modality registration??
Or does it matter??
Grace
----------------------------------------------------------------------------
-----
0 -0.440609 [0.0559426, 0.104512, -0.0340259, 3.98082, -2.79831, 2.1402]
1 -0.458077 [0.0237338, 0.074854, -0.0175932, 5.21787, -4.36703, 2.1402]
2 -0.444355 [0.0407254, 0.043635, -0.0228596, 4.92513, -3.41354, 2.1402]
3 -0.442927 [0.0535433, 0.000940102, -0.0426362, 5.67537, -2.75961, 2.1402]
4 -0.447368 [0.046386, 0.0145486, -0.0167889, 5.19313, -2.8772, 2.1402]
5 -0.450611 [0.0367333, 0.0255149, -0.00895245, 4.76924, -3.14028, 2.1402]
6 -0.480234 [0.0273652, 0.0338326, 0.010831, 4.38405, -3.45562, 2.1402]
7 -0.491004 [0.02514, 0.0276529, 0.0195927, 4.60897, -3.56254, 2.1402]
8 -0.5167 [0.0196279, 0.0256835, 0.0341163, 4.70212, -3.79241, 2.1402]
9 -0.517608 [0.0221036, 0.0190742, 0.0328978, 4.9453, -3.73621, 2.1402]
10 -0.518191 [0.0211846, 0.02218, 0.0420499, 4.88552, -3.84425, 2.1402]
11 -0.52135 [0.0208545, 0.0206294, 0.0415968, 4.94422, -3.86546, 2.1402]
12 -0.523347 [0.0204449, 0.0191823, 0.0413636, 5.00101, -3.89139, 2.1402]
13 -0.52123 [0.0211378, 0.0185994, 0.04133, 5.05607, -3.9209, 2.1402]
14 -0.518093 [0.0197732, 0.018755, 0.0435616, 5.06313, -3.98278, 2.1402]
15 -0.521895 [0.0201374, 0.0178134, 0.0462639, 5.12112, -4.00537, 2.1402]
16 -0.52065 [0.0189278, 0.0179555, 0.0481575, 5.13278, -4.0666, 2.1402]
17 -0.521602 [0.018905, 0.0169584, 0.049328, 5.1897, -4.09224, 2.1402]
18 -0.52087 [0.019628, 0.017331, 0.0545369, 5.23946, -4.12855, 2.1402]
19 -0.5204 [0.018108, 0.0172796, 0.0563701, 5.25343, -4.18928, 2.1402]
20 -0.520973 [0.0177511, 0.0166056, 0.0577169, 5.30406, -4.2258, 2.1402]
21 -0.521113 [0.0175278, 0.0177341, 0.0579758, 5.27651, -4.24036, 2.1402]
22 -0.521249 [0.0201751, 0.0204605, 0.0574916, 5.27965, -4.22711, 2.1402]
23 -0.522455 [0.0201017, 0.0202894, 0.0575649, 5.28657, -4.23073, 2.1402]
24 -0.522845 [0.0200306, 0.0201182, 0.0576416, 5.2935, -4.2343, 2.1402]
25 -0.523012 [0.0199572, 0.0199502, 0.0577323, 5.30037, -4.23801, 2.1402]
26 -0.523017 [0.019885, 0.0197831, 0.0578181, 5.30723, -4.24171, 2.1402]
27 -0.522595 [0.0197908, 0.0196344, 0.0579005, 5.31359, -4.24625, 2.1402]
28 -0.522674 [0.0197011, 0.019487, 0.0579754, 5.31998, -4.25072, 2.1402]
29 -0.521521 [0.0196008, 0.0193495, 0.0581019, 5.32608, -4.25558, 2.1402]
30 -0.521485 [0.019483, 0.0192236, 0.0582602, 5.33178, -4.26091, 2.1402]
31 -0.522264 [0.0193449, 0.0191302, 0.058358, 5.33658, -4.26705, 2.1402]
32 -0.522681 [0.0191987, 0.0190598, 0.0584676, 5.34094, -4.27352, 2.1402]
33 -0.522735 [0.019052, 0.0190024, 0.0585873, 5.34506, -4.28015, 2.1402]
34 -0.522813 [0.0188757, 0.019, 0.0586345, 5.34733, -4.28762, 2.1402]
35 -0.522514 [0.0187408, 0.0190399, 0.0584218, 5.34984, -4.295, 2.1402]
36 -0.521768 [0.0187707, 0.0191056, 0.0580022, 5.35557, -4.30025, 2.1402]
37 -0.52162 [0.0187711, 0.0189871, 0.0578692, 5.36285, -4.30304, 2.1402]
38 -0.521197 [0.0187743, 0.0189535, 0.0577763, 5.36943, -4.30726, 2.1402]
39 -0.521503 [0.0187491, 0.0189253, 0.0576104, 5.37551, -4.31215, 2.1402]
40 -0.52184 [0.0187632, 0.0188649, 0.0576716, 5.3825, -4.31564, 2.1402]
41 -0.52203 [0.0188227, 0.0189083, 0.0572787, 5.38909, -4.31975, 2.1402]
42 -0.521454 [0.0187143, 0.0189169, 0.0573131, 5.39284, -4.3266, 2.1402]
43 -0.521827 [0.0186828, 0.0189044, 0.0573726, 5.39863, -4.33185, 2.1402]
44 -0.521805 [0.0186981, 0.018911, 0.0573623, 5.40497, -4.33641, 2.1402]
45 -0.521772 [0.0187554, 0.0189015, 0.0573842, 5.41202, -4.33977, 2.1402]
46 -0.521918 [0.0188327, 0.0188701, 0.0573479, 5.41947, -4.34214, 2.1402]
47 -0.522521 [0.018739, 0.0188427, 0.0574597, 5.42425, -4.34831, 2.1402]
48 -0.521881 [0.0187046, 0.0190295, 0.0572338, 5.42603, -4.35589, 2.1402]
49 -0.521567 [0.0186666, 0.0190674, 0.0573927, 5.4308, -4.36207, 2.1402]
0 -0.389391 [0.0187093, 0.0191248, 0.0573509, 5.42544, -4.35649, 2.14127]
1 -0.389505 [0.0187492, 0.0191828, 0.0573081, 5.41998, -4.35101, 2.14234]
2 -0.389436 [0.018788, 0.0192422, 0.0572655, 5.41448, -4.34557, 2.14341]
3 -0.389401 [0.0188278, 0.0193, 0.0572215, 5.40901, -4.34009, 2.14446]
4 -0.389451 [0.018868, 0.0193577, 0.0571799, 5.40352, -4.33464, 2.1455]
5 -0.389498 [0.0189076, 0.0194122, 0.0571426, 5.39805, -4.32915, 2.14653]
6 -0.389495 [0.0189478, 0.0194658, 0.0571099, 5.39261, -4.32365, 2.14757]
7 -0.389489 [0.0189853, 0.0195208, 0.0570757, 5.38713, -4.31818, 2.14864]
8 -0.38948 [0.0190242, 0.0195751, 0.0570417, 5.38172, -4.31265, 2.14971]
9 -0.389535 [0.019064, 0.0196318, 0.056998, 5.37632, -4.30711, 2.15077]
10 -0.389525 [0.0191047, 0.0196881, 0.0569549, 5.37095, -4.30154, 2.15183]
11 -0.389573 [0.0191432, 0.0197446, 0.0569186, 5.36554, -4.29601, 2.15292]
12 -0.389567 [0.0191819, 0.0198004, 0.0568823, 5.36006, -4.29055, 2.154]
13 -0.389627 [0.019221, 0.0198535, 0.0568449, 5.35467, -4.285, 2.15508]
14 -0.389533 [0.0192599, 0.0199053, 0.0568056, 5.34927, -4.27946, 2.15615]
15 -0.389808 [0.0193003, 0.0199556, 0.0567747, 5.34391, -4.27387, 2.15722]
16 -0.390001 [0.0193408, 0.0200036, 0.0567476, 5.3386, -4.26826, 2.15831]
17 -0.390044 [0.0193779, 0.0200513, 0.0567296, 5.33321, -4.26271, 2.15943]
18 -0.390089 [0.019414, 0.0201015, 0.056709, 5.32774, -4.25725, 2.16053]
19 -0.390178 [0.0194489, 0.0201502, 0.0566886, 5.32228, -4.25178, 2.16166]
20 -0.390225 [0.0194831, 0.0201989, 0.0566697, 5.31684, -4.24628, 2.1628]
21 -0.390266 [0.0195176, 0.0202466, 0.0566484, 5.31149, -4.24071, 2.16393]
22 -0.390314 [0.0195499, 0.0202953, 0.0566288, 5.3061, -4.23517, 2.16508]
23 -0.390361 [0.0195833, 0.0203445, 0.0566087, 5.30071, -4.22963, 2.16621]
24 -0.390561 [0.0196143, 0.0203937, 0.0565863, 5.29526, -4.22415, 2.16735]
25 -0.390608 [0.0196454, 0.0204433, 0.0565666, 5.28983, -4.21866, 2.16851]
26 -0.390654 [0.0196803, 0.0204966, 0.0565403, 5.28438, -4.21317, 2.16963]
27 -0.390701 [0.0197127, 0.02055, 0.0565067, 5.27897, -4.20765, 2.17075]
28 -0.390748 [0.0197464, 0.020604, 0.0564753, 5.27358, -4.20211, 2.17187]
29 -0.390799 [0.019781, 0.0206587, 0.0564385, 5.26825, -4.19652, 2.17301]
30 -0.390924 [0.0198148, 0.020714, 0.0564045, 5.26291, -4.19093, 2.17415]
31 -0.390978 [0.0198506, 0.0207689, 0.0563671, 5.25765, -4.18527, 2.1753]
32 -0.391097 [0.0198857, 0.020823, 0.0563262, 5.25239, -4.17961, 2.17645]
33 -0.391153 [0.0199238, 0.0208752, 0.0562792, 5.24712, -4.17396, 2.17758]
34 -0.391209 [0.0199613, 0.0209244, 0.0562253, 5.24197, -4.16818, 2.17865]
35 -0.39122 [0.0199967, 0.0209734, 0.0561743, 5.23681, -4.16243, 2.17975]
36 -0.391208 [0.0200331, 0.0210228, 0.0561208, 5.23162, -4.15669, 2.18084]
37 -0.391263 [0.0200683, 0.0210748, 0.0560653, 5.22649, -4.15091, 2.18197]
38 -0.391314 [0.0201025, 0.0211254, 0.0560108, 5.22138, -4.14512, 2.18313]
39 -0.391315 [0.0201379, 0.021176, 0.0559565, 5.21633, -4.13928, 2.1843]
40 -0.391372 [0.020176, 0.0212252, 0.0559052, 5.21137, -4.13336, 2.18546]
41 -0.39142 [0.0202143, 0.0212756, 0.0558562, 5.20641, -4.12743, 2.18663]
42 -0.391472 [0.0202572, 0.0213236, 0.0558088, 5.20158, -4.12141, 2.18776]
43 -0.391519 [0.0202995, 0.0213715, 0.0557598, 5.19674, -4.11538, 2.18891]
44 -0.391574 [0.0203443, 0.0214211, 0.0557124, 5.19194, -4.10932, 2.19005]
45 -0.39162 [0.0203885, 0.0214708, 0.0556644, 5.18715, -4.10327, 2.19121]
46 -0.39167 [0.0204329, 0.0215207, 0.0556168, 5.18235, -4.09721, 2.19238]
47 -0.391716 [0.0204762, 0.0215718, 0.0555651, 5.17761, -4.09111, 2.19353]
48 -0.391721 [0.0205174, 0.0216242, 0.0555157, 5.17278, -4.08509, 2.19469]
49 -0.391766 [0.0205587, 0.0216747, 0.0554653, 5.1679, -4.0791, 2.19585]
0 -0.293629 [0.0206299, 0.0217326, 0.0554804, 5.16151, -4.07467, 2.19658]
1 -0.293697 [0.0206962, 0.0217884, 0.055503, 5.1551, -4.07027, 2.1973]
2 -0.293755 [0.0207634, 0.0218441, 0.0555272, 5.14872, -4.06583, 2.19804]
3 -0.293865 [0.0208278, 0.0218952, 0.0555421, 5.14225, -4.0615, 2.19877]
4 -0.29399 [0.0208918, 0.0219462, 0.0555567, 5.13578, -4.05718, 2.19944]
5 -0.294044 [0.0209524, 0.0219982, 0.055583, 5.12937, -4.05278, 2.20014]
6 -0.2941 [0.021012, 0.022049, 0.0556144, 5.12296, -4.04837, 2.20085]
7 -0.294231 [0.0210701, 0.0221001, 0.0556433, 5.11657, -4.04393, 2.20157]
8 -0.294284 [0.0211241, 0.0221552, 0.0556723, 5.1101, -4.03961, 2.20229]
9 -0.294336 [0.0211772, 0.022211, 0.0557008, 5.10361, -4.03534, 2.20301]
10 -0.294362 [0.0212304, 0.0222671, 0.0557319, 5.09703, -4.03118, 2.20374]
11 -0.294546 [0.0212832, 0.0223233, 0.0557612, 5.09049, -4.02698, 2.20447]
12 -0.29457 [0.0213338, 0.022381, 0.0557899, 5.08392, -4.02283, 2.20521]
13 -0.294619 [0.0213839, 0.0224376, 0.0558198, 5.07738, -4.01861, 2.20592]
14 -0.294669 [0.0214307, 0.0224928, 0.0558477, 5.07081, -4.01445, 2.20664]
15 -0.29465 [0.0214796, 0.0225477, 0.0558771, 5.06424, -4.01028, 2.20734]
16 -0.294699 [0.0215272, 0.0226027, 0.0559079, 5.05764, -4.00618, 2.2081]
17 -0.294962 [0.0215761, 0.0226595, 0.0559332, 5.05102, -4.0021, 2.20883]
18 -0.295011 [0.0216243, 0.0227165, 0.0559536, 5.04442, -3.99798, 2.20959]
19 -0.295061 [0.0216733, 0.0227738, 0.0559782, 5.03782, -3.99387, 2.21035]
20 -0.295111 [0.0217199, 0.0228307, 0.0560019, 5.03123, -3.98976, 2.21113]
21 -0.295158 [0.021766, 0.0228869, 0.0560243, 5.02464, -3.98564, 2.2119]
22 -0.295178 [0.02181, 0.0229439, 0.0560421, 5.01805, -3.98152, 2.21266]
23 -0.295273 [0.0218512, 0.0230004, 0.0560632, 5.0114, -3.97749, 2.21345]
24 -0.29532 [0.0218946, 0.0230556, 0.0560855, 5.00476, -3.97346, 2.21427]
25 -0.295368 [0.0219363, 0.023112, 0.0561085, 4.99813, -3.96941, 2.2151]
26 -0.295483 [0.0219783, 0.0231686, 0.0561281, 4.99155, -3.9653, 2.21596]
27 -0.295485 [0.0220189, 0.0232239, 0.0561398, 4.98503, -3.96109, 2.21684]
28 -0.295535 [0.0220623, 0.0232788, 0.0561568, 4.97855, -3.95681, 2.21771]
29 -0.295583 [0.0220982, 0.023335, 0.0562, 4.97201, -3.95265, 2.21866]
30 -0.295633 [0.0221333, 0.0233902, 0.0562436, 4.9655, -3.94844, 2.21962]
31 -0.295679 [0.0221686, 0.0234457, 0.0562842, 4.95903, -3.94417, 2.22058]
32 -0.295695 [0.0222031, 0.0235014, 0.0563223, 4.95256, -3.9399, 2.22155]
33 -0.295744 [0.0222382, 0.0235583, 0.0563625, 4.94598, -3.9358, 2.22248]
34 -0.295791 [0.0222705, 0.023615, 0.0564006, 4.93936, -3.93175, 2.2234]
35 -0.295842 [0.0223052, 0.0236725, 0.0564318, 4.93278, -3.92765, 2.22434]
36 -0.295987 [0.0223366, 0.0237306, 0.0564639, 4.92614, -3.92366, 2.2253]
37 -0.296163 [0.0223679, 0.0237889, 0.0564979, 4.91948, -3.91969, 2.22627]
38 -0.296211 [0.0224004, 0.0238459, 0.0565363, 4.91287, -3.91564, 2.22724]
39 -0.29621 [0.022432, 0.0239043, 0.0565776, 4.90623, -3.91164, 2.22825]
40 -0.296259 [0.0224627, 0.0239626, 0.056617, 4.89959, -3.90766, 2.22925]
41 -0.296308 [0.0224945, 0.0240195, 0.0566568, 4.89299, -3.9036, 2.23027]
42 -0.296357 [0.0225259, 0.024077, 0.0566959, 4.88635, -3.89962, 2.23129]
43 -0.29642 [0.0225552, 0.0241341, 0.0567328, 4.87972, -3.89563, 2.23233]
44 -0.296469 [0.0225819, 0.0241912, 0.0567734, 4.8731, -3.89162, 2.2334]
45 -0.296519 [0.0226042, 0.0242463, 0.0568092, 4.86641, -3.88774, 2.23447]
46 -0.296568 [0.0226277, 0.0243018, 0.0568419, 4.8597, -3.88389, 2.23555]
47 -0.296619 [0.0226534, 0.0243579, 0.056879, 4.85302, -3.87999, 2.23663]
48 -0.296666 [0.0226835, 0.0244134, 0.0569233, 4.84643, -3.87592, 2.23767]
49 -0.296714 [0.0227186, 0.0244661, 0.056984, 4.83996, -3.87168, 2.23874]
----------------------------------------------------------------------------
-----------------------------
----- Original Message -----
From: "Karthik Krishnan" <Karthik.Krishnan at kitware.com>
To: "Grace Chen" <Grace.Chen at swri.ca>
Cc: "Kajetan Berlinger" <kaje at kaje.info>; <insight-users at itk.org>
Sent: Wednesday, May 31, 2006 10:53 AM
Subject: Re: [Insight-users] Re:
itkmattesMutualInformationImagetoImageMetric
> Grace,
>
> Yes, As Kajetan mentioned, the Mattes metric needs to be minimized. This
> may sound counterintuitive since Mutual information defined as :
>
> I(X,Y) = H(X) + H(Y) - H(X,Y) has got to be positive,
>
> Yet the mattes yeilds values that are negative. Read the last line of
> the GetValue() method in
> itkMattesMutualInformationImageToImageMetric.txx. It reads :
> return static_cast<MeasureType>( -1.0 * sum );
> In other words the classical MI value is negated.
>
> The other mutual information metrics though, need to be maximized. For
> instance, if you use itk::MutualInformationHistogramImageToImageMetric,
> you will have to maximize it.
>
> HTH
> -karthik
>
> Kajetan Berlinger wrote:
>
> > Hi Grace,
> >
> > does your optimizer maximize or minimize?
> > Use optimizer->SetMinimize(true) to make sure.
> >
> > cu
> > kaj
> >
> >
> > Grace Chen wrote:
> >
> >> Hi,
> >>
> >>> From the ITK software guide, it seems to indicate that the metric
> >>> value should get smaller (more negative) as the iteration
> >>> increases. However, that's not the case for me....
> >>
> >>
> >> I am very confused. I've posted many questions here but no
> >> answer.... Please help!!
> >>
> >> Thanx!!
> >>
> >>
> >> Grace
> >>
> >> ----- Original Message ----- From: Grace Chen To:
> >> insight-users at itk.org Sent: Tuesday, May 30, 2006 4:44 PM
> >> Subject: itkmattesMutualInformationImagetoImageMetric
> >>
> >>
> >> Hi there,
> >>
> >> I am using itkmattesMutualInformationImagetoImageMetric, and I am
> >> wondering if the mutual information value approaching toward 0 (it's
> >> always negative), does that mean a better regirtration transformation?
> >>
> >> Thanx!
> >>
> >> Grace
> >>
> >>
>
>> ------------------------------------------------------------------------
> >>
> >> _______________________________________________
> >> Insight-users mailing list
> >> Insight-users at itk.org
> >> http://www.itk.org/mailman/listinfo/insight-users
> >
> >
> > _______________________________________________
> > Insight-users mailing list
> > Insight-users at itk.org
> > http://www.itk.org/mailman/listinfo/insight-users
> >
>
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