::PrintType>(label)
<< " is the background label.");
```
in file:
`d:\Project\SimpleITK\SuperBuild\bin\ITK\Modules\Filtering\LabelMap\include\itkLabelMap.hxx`
What I have done wrong ? Can you help me a little bit ?
Regards,
Flaviu.
---
### Author:  [@blowekamp](https://discourse.itk.org/u/blowekamp)
#### Post date: [March 19, 2020, 12:42pm UTC](https://discourse.itk.org/t/labelintensitystatisticsimagefilter-error/2852/2 "2020-03-19T12:42:29Z")
Including the actual error message with the value of label would be helpful.
You are using the index `I` not the value in the `labels` array.
Here is a C++ [for range loop](https://en.cppreference.com/w/cpp/language/range-for) to iterate over the available labels:
```auto
for (auto label: statistics.GetLabels())
{
std::cout << "Label: " << label << " Mean: " << statistics.GetMean(label) << std::end;
}
```
---
### Author:  [@flaviu2](https://discourse.itk.org/u/flaviu2)
#### Post date: [March 19, 2020, 1:21pm UTC](https://discourse.itk.org/t/labelintensitystatisticsimagefilter-error/2852/3 "2020-03-19T13:21:57Z")
I am trying to loop without **auto** specifier.
---
### Author:  [@flaviu2](https://discourse.itk.org/u/flaviu2)
#### Post date: [March 19, 2020, 2:21pm UTC](https://discourse.itk.org/t/labelintensitystatisticsimagefilter-error/2852/4 "2020-03-19T14:21:23Z")
Here is my testing code:
```
std::vector::iterator it;
std::vector labels = statistics.GetLabels();
for (it = labels.begin(); it != labels.end(); ++it)
{
TRACE(_T("Label: {%lu} -> Mean: {%f} Size: {%f}\n"),
*it, statistics.GetMean(*it), statistics.GetPhysicalSize(*it));
}
```
I know, it is more ugly code, but for, is more transparent 🙂
Here is the result:
**Label: {1} -\> Mean: {1049.634854} Size: {179605.944000}**
@blowekamp I have run this code on a _CBCT dicom_ (a mandibula). And I’ve ran this because I intend to use it to know how to segment a bone from this vtkVolume … how can I use this data from **LabelIntensityStatisticsImageFilter** to remove a bone from this volume ?
---
### Author:  [@dzenanz](https://discourse.itk.org/u/dzenanz)
#### Post date: [March 19, 2020, 3:57pm UTC](https://discourse.itk.org/t/labelintensitystatisticsimagefilter-error/2852/5 "2020-03-19T15:57:07Z")
The answer to your original question is to start from 1, not 0:
`for (int64_t i = 1; ...`
One way of “removing” the bone (assuming label 1 is bone) is to mask it away. First apply `Not` filter, then `Mask` filter.
---
### Author:  [@flaviu2](https://discourse.itk.org/u/flaviu2)
#### Post date: [March 19, 2020, 3:59pm UTC](https://discourse.itk.org/t/labelintensitystatisticsimagefilter-error/2852/6 "2020-03-19T15:59:24Z")
With following code:
```
sitk::Image cc = sitk::SLIC(imgTemp);
sitk::LabelIntensityStatisticsImageFilter statistics;
statistics.Execute(cc, imgTemp);
std::vector::iterator it;
std::vector labels = statistics.GetLabels();
for (it = labels.begin(); it != labels.end(); ++it)
{
TRACE(_T("Label: {%lu} -> Mean: {%f} Size: {%f}\n"),
*it, statistics.GetMean(*it), statistics.GetPhysicalSize(*it));
}
```
I got:
```
Label: {1} -> Mean: {368.290207} Size: {566.464000}
Label: {2} -> Mean: {2526.748027} Size: {1077.480000}
Label: {3} -> Mean: {1322.812512} Size: {2101.808000}
Label: {4} -> Mean: {5.314739} Size: {1655.520000}
Label: {5} -> Mean: {1152.650079} Size: {2021.784000}
Label: {7} -> Mean: {1261.300340} Size: {1532.264000}
Label: {8} -> Mean: {0.115451} Size: {1778.208000}
Label: {9} -> Mean: {1447.813594} Size: {2024.352000}
Label: {10} -> Mean: {5568.465393} Size: {298.552000}
Label: {11} -> Mean: {241.681532} Size: {711.432000}
Label: {12} -> Mean: {1184.535699} Size: {2184.616000}
Label: {13} -> Mean: {1004.544586} Size: {1606.856000}
Label: {14} -> Mean: {1537.574133} Size: {1212.360000}
Label: {16} -> Mean: {1125.041260} Size: {1670.584000}
Label: {17} -> Mean: {909.822657} Size: {1916.560000}
Label: {18} -> Mean: {571.987740} Size: {1087.808000}
Label: {19} -> Mean: {2970.865338} Size: {569.248000}
Label: {20} -> Mean: {2112.483121} Size: {948.616000}
Label: {21} -> Mean: {855.563037} Size: {1174.800000}
Label: {22} -> Mean: {279.319568} Size: {668.728000}
Label: {23} -> Mean: {796.453627} Size: {1563.768000}
Label: {25} -> Mean: {783.731848} Size: {2054.328000}
Label: {26} -> Mean: {236.623219} Size: {592.832000}
Label: {27} -> Mean: {578.980189} Size: {1630.184000}
Label: {28} -> Mean: {1074.661212} Size: {2048.784000}
Label: {29} -> Mean: {471.110132} Size: {728.288000}
Label: {30} -> Mean: {33.103330} Size: {496.504000}
Label: {31} -> Mean: {582.654392} Size: {1093.424000}
Label: {32} -> Mean: {3004.682777} Size: {1008.576000}
Label: {33} -> Mean: {1502.536099} Size: {599.576000}
Label: {34} -> Mean: {932.294998} Size: {1759.440000}
Label: {35} -> Mean: {672.321851} Size: {1433.184000}
Label: {36} -> Mean: {1.081547} Size: {1344.888000}
Label: {38} -> Mean: {1601.040743} Size: {846.872000}
Label: {39} -> Mean: {154.552404} Size: {489.584000}
Label: {41} -> Mean: {616.459449} Size: {972.712000}
Label: {42} -> Mean: {1158.360874} Size: {1430.040000}
Label: {43} -> Mean: {420.245586} Size: {973.768000}
Label: {44} -> Mean: {0.013475} Size: {1269.888000}
Label: {45} -> Mean: {0.002492} Size: {1804.240000}
Label: {46} -> Mean: {3.379783} Size: {1379.608000}
Label: {47} -> Mean: {595.509167} Size: {1584.880000}
Label: {48} -> Mean: {876.348265} Size: {1709.616000}
Label: {49} -> Mean: {741.060466} Size: {1919.760000}
Label: {50} -> Mean: {7.467693} Size: {409.072000}
Label: {51} -> Mean: {888.895067} Size: {1505.272000}
Label: {52} -> Mean: {5.131299} Size: {834.432000}
Label: {53} -> Mean: {0.000000} Size: {1877.896000}
Label: {54} -> Mean: {0.000000} Size: {1603.504000}
Label: {55} -> Mean: {0.000000} Size: {1781.232000}
Label: {56} -> Mean: {2.317372} Size: {1365.440000}
Label: {57} -> Mean: {539.526680} Size: {1343.616000}
Label: {58} -> Mean: {0.032741} Size: {493.568000}
Label: {59} -> Mean: {603.087515} Size: {1825.872000}
Label: {60} -> Mean: {1.179970} Size: {1170.104000}
Label: {61} -> Mean: {0.000000} Size: {1949.160000}
Label: {62} -> Mean: {0.000000} Size: {1583.984000}
Label: {63} -> Mean: {6.384759} Size: {1738.128000}
Label: {64} -> Mean: {968.834762} Size: {946.952000}
Label: {65} -> Mean: {1173.982044} Size: {2118.912000}
Label: {66} -> Mean: {1282.010115} Size: {1144.464000}
Label: {67} -> Mean: {2.750152} Size: {1684.992000}
Label: {68} -> Mean: {1828.256807} Size: {1617.560000}
Label: {69} -> Mean: {744.446380} Size: {1679.000000}
Label: {70} -> Mean: {2429.636374} Size: {1005.144000}
Label: {71} -> Mean: {0.000000} Size: {1710.064000}
Label: {72} -> Mean: {1037.654082} Size: {1918.584000}
Label: {73} -> Mean: {2335.705597} Size: {1238.848000}
Label: {74} -> Mean: {1872.602247} Size: {1250.224000}
Label: {76} -> Mean: {1251.685430} Size: {2535.880000}
Label: {77} -> Mean: {1729.013019} Size: {752.720000}
Label: {79} -> Mean: {1082.833144} Size: {2276.024000}
Label: {80} -> Mean: {701.851550} Size: {1716.024000}
Label: {81} -> Mean: {669.559224} Size: {2075.304000}
Label: {82} -> Mean: {1288.527396} Size: {1615.408000}
Label: {83} -> Mean: {1477.291494} Size: {1083.904000}
Label: {85} -> Mean: {1082.230514} Size: {1657.272000}
Label: {86} -> Mean: {1118.916965} Size: {1926.704000}
Label: {87} -> Mean: {1906.287068} Size: {786.880000}
Label: {88} -> Mean: {1000.200725} Size: {1945.192000}
Label: {90} -> Mean: {477.780360} Size: {794.464000}
Label: {91} -> Mean: {824.415658} Size: {2400.936000}
Label: {92} -> Mean: {962.614150} Size: {1597.824000}
Label: {93} -> Mean: {2633.126717} Size: {952.040000}
Label: {94} -> Mean: {1651.431359} Size: {915.784000}
Label: {95} -> Mean: {2376.183570} Size: {1473.704000}
Label: {96} -> Mean: {738.362924} Size: {1150.544000}
Label: {97} -> Mean: {604.685956} Size: {1862.136000}
Label: {98} -> Mean: {633.751769} Size: {2076.648000}
Label: {99} -> Mean: {1.038663} Size: {1394.008000}
Label: {101} -> Mean: {716.292855} Size: {1208.872000}
Label: {102} -> Mean: {731.980081} Size: {1546.664000}
Label: {104} -> Mean: {671.521394} Size: {1091.872000}
Label: {107} -> Mean: {0.000000} Size: {1529.456000}
Label: {108} -> Mean: {0.000000} Size: {1536.248000}
Label: {109} -> Mean: {3.321768} Size: {1747.816000}
Label: {110} -> Mean: {571.734155} Size: {993.272000}
Label: {113} -> Mean: {632.352636} Size: {1812.792000}
Label: {114} -> Mean: {680.077052} Size: {2278.672000}
Label: {115} -> Mean: {0.000000} Size: {1759.840000}
Label: {116} -> Mean: {0.000000} Size: {1483.624000}
Label: {117} -> Mean: {0.000000} Size: {1526.600000}
Label: {118} -> Mean: {0.000000} Size: {1561.400000}
Label: {119} -> Mean: {4.027392} Size: {1361.552000}
Label: {120} -> Mean: {609.241966} Size: {1692.272000}
Label: {121} -> Mean: {691.843311} Size: {1788.968000}
Label: {122} -> Mean: {749.687721} Size: {1979.048000}
Label: {123} -> Mean: {0.000000} Size: {1314.408000}
Label: {124} -> Mean: {0.000000} Size: {1525.384000}
Label: {125} -> Mean: {0.000000} Size: {1546.360000}
Label: {126} -> Mean: {4.117650} Size: {1808.216000}
Label: {127} -> Mean: {1164.579878} Size: {2739.072000}
Label: {128} -> Mean: {0.603684} Size: {335.328000}
Label: {129} -> Mean: {1146.404821} Size: {2498.248000}
Label: {130} -> Mean: {44.142602} Size: {1071.960000}
Label: {131} -> Mean: {1149.141517} Size: {2929.448000}
Label: {132} -> Mean: {0.070144} Size: {799.960000}
Label: {133} -> Mean: {1526.197683} Size: {1364.080000}
Label: {134} -> Mean: {0.000000} Size: {1890.400000}
Label: {135} -> Mean: {812.220559} Size: {1869.104000}
Label: {136} -> Mean: {2164.437493} Size: {1284.920000}
Label: {137} -> Mean: {0.008543} Size: {338.992000}
Label: {138} -> Mean: {1099.524489} Size: {2371.176000}
Label: {139} -> Mean: {1139.302470} Size: {3310.896000}
Label: {142} -> Mean: {931.524617} Size: {1739.600000}
Label: {144} -> Mean: {299.822941} Size: {323.824000}
Label: {146} -> Mean: {1770.032962} Size: {860.392000}
Label: {148} -> Mean: {36.471044} Size: {491.504000}
Label: {149} -> Mean: {1128.391914} Size: {1520.272000}
Label: {150} -> Mean: {2217.467941} Size: {864.656000}
Label: {152} -> Mean: {720.425645} Size: {2535.632000}
Label: {153} -> Mean: {4.892183} Size: {374.784000}
Label: {154} -> Mean: {594.911283} Size: {2620.728000}
Label: {155} -> Mean: {786.821474} Size: {1048.048000}
Label: {156} -> Mean: {2235.781010} Size: {1053.600000}
Label: {157} -> Mean: {1245.490110} Size: {1402.608000}
Label: {158} -> Mean: {2509.458932} Size: {603.488000}
Label: {159} -> Mean: {871.754826} Size: {1427.592000}
Label: {160} -> Mean: {9.758531} Size: {488.808000}
Label: {161} -> Mean: {0.000000} Size: {389.744000}
Label: {162} -> Mean: {0.400833} Size: {1269.256000}
Label: {163} -> Mean: {678.296187} Size: {1754.432000}
Label: {164} -> Mean: {3.292190} Size: {630.904000}
Label: {166} -> Mean: {881.339035} Size: {1436.760000}
Label: {168} -> Mean: {571.658143} Size: {1437.184000}
Label: {170} -> Mean: {68.545898} Size: {635.328000}
Label: {171} -> Mean: {0.000000} Size: {1653.208000}
Label: {172} -> Mean: {0.951615} Size: {1614.712000}
Label: {173} -> Mean: {617.069781} Size: {1543.680000}
Label: {175} -> Mean: {765.048832} Size: {2217.896000}
Label: {177} -> Mean: {688.089716} Size: {1644.648000}
Label: {178} -> Mean: {0.532532} Size: {1554.296000}
Label: {179} -> Mean: {0.000000} Size: {2098.216000}
Label: {180} -> Mean: {0.000000} Size: {1554.752000}
Label: {181} -> Mean: {0.000000} Size: {1639.272000}
Label: {182} -> Mean: {0.494761} Size: {1355.096000}
Label: {183} -> Mean: {671.728335} Size: {2137.576000}
Label: {184} -> Mean: {2.433458} Size: {332.840000}
Label: {185} -> Mean: {644.002123} Size: {1303.624000}
Label: {186} -> Mean: {0.000000} Size: {1262.864000}
Label: {187} -> Mean: {0.000000} Size: {1661.632000}
Label: {188} -> Mean: {0.000000} Size: {1576.472000}
Label: {189} -> Mean: {2.432324} Size: {509.544000}
Label: {190} -> Mean: {418.095877} Size: {678.200000}
Label: {191} -> Mean: {383.437455} Size: {650.160000}
Label: {192} -> Mean: {2221.795102} Size: {769.240000}
Label: {193} -> Mean: {1093.841956} Size: {1537.240000}
Label: {194} -> Mean: {1040.406918} Size: {1287.120000}
Label: {195} -> Mean: {785.872931} Size: {1179.456000}
Label: {196} -> Mean: {673.430588} Size: {2443.392000}
Label: {197} -> Mean: {712.819455} Size: {1407.872000}
Label: {198} -> Mean: {1222.519153} Size: {2343.664000}
Label: {199} -> Mean: {490.345090} Size: {638.280000}
Label: {200} -> Mean: {475.100847} Size: {662.704000}
Label: {201} -> Mean: {496.719702} Size: {1401.480000}
Label: {202} -> Mean: {696.450730} Size: {1590.840000}
Label: {203} -> Mean: {1109.066118} Size: {1179.352000}
Label: {204} -> Mean: {2424.302509} Size: {389.040000}
Label: {205} -> Mean: {661.414440} Size: {2436.288000}
Label: {206} -> Mean: {742.483240} Size: {1631.552000}
Label: {207} -> Mean: {923.733392} Size: {537.688000}
Label: {208} -> Mean: {1080.068644} Size: {410.816000}
Label: {209} -> Mean: {643.469645} Size: {258.800000}
```
---
### Author:  [@dzenanz](https://discourse.itk.org/u/dzenanz)
#### Post date: [March 19, 2020, 4:05pm UTC](https://discourse.itk.org/t/labelintensitystatisticsimagefilter-error/2852/7 "2020-03-19T16:05:56Z")
So what you have are super-pixels, and that is only the first step in some ways to do bone segmentation, which is what you want. In order to “remove” the bone, you need to segment it first. And image segmentation, in general form, is an unsolved problem. Perhaps start a new topic with the description of your problem and constraints, if you need some advice.
---
### Author:  [@flaviu2](https://discourse.itk.org/u/flaviu2)
#### Post date: [March 19, 2020, 4:33pm UTC](https://discourse.itk.org/t/labelintensitystatisticsimagefilter-error/2852/8 "2020-03-19T16:33:15Z")
> [@dzenanz](#):
>
> The answer to your original question is to start from 1, not 0:
> `for (int64_t i = 1; ...`
Yes, you had right, I figured out after I trace the variables from _statistics.GetLabels()_.
" One way of “removing” the bone (assuming label 1 is bone) is to mask it away. First apply `Not` filter, then `Mask` filter."
You gave me a hope 🙂 I hope I make it. Perhaps I’ll address some questions here …