# Implementation of a Custom ImageToImage Similarity Metric for Registration

**URL:** https://discourse.itk.org/t/implementation-of-a-custom-imagetoimage-similarity-metric-for-registration/6047
**Category:** Beginner Questions
**Tags:** itk, registration
**Created:** [July 7, 2023, 12:13pm UTC](https://discourse.itk.org/t/implementation-of-a-custom-imagetoimage-similarity-metric-for-registration/6047 "2023-07-07T12:13:32Z")
**Posts on this page:** 6
**Page:** 1

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### Author: ![JRopers](https://discourse.itk.org/letter_avatar_proxy/v4/letter/j/77aa72/32.png) [@JRopers](https://discourse.itk.org/u/JRopers)
#### Post date: [July 7, 2023, 12:13pm UTC](https://discourse.itk.org/t/implementation-of-a-custom-imagetoimage-similarity-metric-for-registration/6047/1 "2023-07-07T12:13:32Z")

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Hello ITK community! I am in desperate need for help as I am finding no resources on this.

I am trying to implement a custom ImageToImage similarity metric for the purpose of registration. For a project, I want to register two 3D binary segmentation masks as a first attempt. For this, I want a simple DICE metric to be used as the similarity metric, however, this does not exist in the C++ ITK library. How do I implement this? So far I simply tried to inherit from the ImageToImageMetricv4 class and implement my own value and derivative calculation (see implementation below) but I just keep getting errors that I honestly do not quite understand. Can anybody help me out here? Or at least point me in the right direction on how to do this?

```c++
template <typename TFixedImage, typename TMovingImage>
class DiceMetric : public itk::ImageToImageMetricv4<TFixedImage, TMovingImage>
{
    public:
        using Self = DiceMetric;
        using Superclass = itk::ImageToImageMetricv4<TFixedImage, TMovingImage>;
        using Pointer = itk::SmartPointer<Self>;

        itkTypeMacro(DiceMetric, ImageToImageMetricv4);

        // Define some type aliases for convenience
        using FixedImageType = TFixedImage;
        using MovingImageType = TMovingImage;
        using FixedImagePixelType = typename FixedImageType::PixelType;
        using MovingImagePixelType = typename MovingImageType::PixelType;
        using MeasureType = typename Superclass::MeasureType;
        using DerivativeType = typename Superclass::DerivativeType;
        using FixedImageRegionType = typename FixedImageType::RegionType;

    // Override GetValue function
    MeasureType GetValue() const override
    {
        // Get the fixed and moving images
        const FixedImageType *fixedImage = this->GetFixedImage();
        const MovingImageType *movingImage = this->GetMovingImage();

        // Get the region of overlap between the two images
        FixedImageRegionType region = fixedImage->GetBufferedRegion();
        region.Crop(movingImage->GetBufferedRegion());

        // Compute the Dice coefficient
        MeasureType dice = 0.0;
        MeasureType intersection = 0.0;
        MeasureType sum = 0.0;

        itk::ImageRegionConstIterator<FixedImageType> fixedIt(fixedImage, region);
        itk::ImageRegionConstIterator<MovingImageType> movingIt(movingImage, region);

        while (!fixedIt.IsAtEnd())
        {
            FixedImagePixelType fixedValue = fixedIt.Get();
            MovingImagePixelType movingValue = movingIt.Get();

            intersection += static_cast<MeasureType>(fixedValue && movingValue);
            sum += static_cast<MeasureType>(fixedValue || movingValue);

            ++fixedIt;
            ++movingIt;
        }

        if (sum > 0.0)
        {
            dice = (2.0 * intersection) / sum;
        }

        return dice;
    }

    // Override GetDerivative function
    void GetDerivative(DerivativeType &derivative) const override
    {
        // Set the derivative to zero manually
        derivative.Fill(0.0);
    }

    protected:
    DiceMetric() = default;
    ~DiceMetric() override = default;
};

// Command Observer to print optimizer paramters at each iteration
// to monitor the evolution of the registration process
class CommandIterationUpdate : public itk::Command {
    public:
        using Self = CommandIterationUpdate;
        using Superclass = itk::Command;
        using Pointer = itk::SmartPointer<Self>;
        itkNewMacro(Self);
    
    protected:
        CommandIterationUpdate() = default;
    
    public:
        using OptimizerType = itk::RegularStepGradientDescentOptimizerv4<double>;
        using OptimizerPointer = const OptimizerType *;
        void Execute(itk::Object * caller, const itk::EventObject & event) override {
            Execute((const itk::Object *)caller, event);
        }
        void Execute(const itk::Object * object, const itk::EventObject & event) override {
            auto optimizer = static_cast<OptimizerPointer>(object);
            if (!itk::IterationEvent().CheckEvent(&event)) {
                return;
            }
            std::cout << optimizer->GetCurrentIteration() << " ";
            std::cout << optimizer->GetValue() << " ";
            std::cout << optimizer->GetCurrentPosition() << std::endl;
        }
};

```

When I then try to set this metric in the registration method with  
registration-\>SetMetric(metric)  
I get the error:

no suitable conversion function from “itk::Object::Pointer” to "itk::ObjectToObjectMetricBaseTemplate

Any help is appreciated!

---

<div class="post-metadata">

### Author: ![dzenanz](https://discourse.itk.org/user_avatar/discourse.itk.org/dzenanz/32/1093_2.png) [@dzenanz](https://discourse.itk.org/u/dzenanz)
#### Post date: [July 7, 2023, 1:51pm UTC](https://discourse.itk.org/t/implementation-of-a-custom-imagetoimage-similarity-metric-for-registration/6047/2 "2023-07-07T13:51:55Z")

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There are two separate registration frameworks within ITK, v3 (classes with no suffix), and v4 (classes with v4 suffix). You might be mixing them up.

[MatchCardinality](https://itk.org/Doxygen/html/classitk_1_1MatchCardinalityImageToImageMetric.html) is something like a Dice metric. Why don’t you give that a try?

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<div class="post-metadata">

### Author: ![JRopers](https://discourse.itk.org/letter_avatar_proxy/v4/letter/j/77aa72/32.png) [@JRopers](https://discourse.itk.org/u/JRopers)
#### Post date: [July 8, 2023, 11:00am UTC](https://discourse.itk.org/t/implementation-of-a-custom-imagetoimage-similarity-metric-for-registration/6047/3 "2023-07-08T11:00:19Z")

</div>

That actually sounds great! Just to make sure I understand this correctly, MatchCardinality is only available in v3? So this means I can also only use it with v3 registration components?

---

<div class="post-metadata">

### Author: ![JRopers](https://discourse.itk.org/letter_avatar_proxy/v4/letter/j/77aa72/32.png) [@JRopers](https://discourse.itk.org/u/JRopers)
#### Post date: [July 8, 2023, 12:00pm UTC](https://discourse.itk.org/t/implementation-of-a-custom-imagetoimage-similarity-metric-for-registration/6047/4 "2023-07-08T12:00:25Z")

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So I managed to use the MatchCardinality Metric with only v3 components, which is great! However, I now have a problem that I have not seen before and it seems to be due to the interpolator I am using. I have tried both itk::LinearInterpolateImageFunction and itk::NearestNeighborInterpolateImageFunction and for both I get the same artifact.

The original input binary mask of the lung looks like this:

 ![OriginalLung](https://discourse.itk.org/uploads/default/original/2X/f/f62c47618624ea327b66ce36d71c30dd51ebdfc6.jpeg)

After registration, the registered binary mask of the lung has this weird artifact:

 ![RegisteredLung](https://discourse.itk.org/uploads/default/original/2X/9/959a5dc1f3146c109482e493e168855ec6858b68.jpeg)

It is almost like a “wall” built along the coordinate system axis. What could be causing this and how do I avoid it? It is obviously a problem if I am registering along multiple iterations and this artifact starts showing up.

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<div class="post-metadata">

### Author: ![JRopers](https://discourse.itk.org/letter_avatar_proxy/v4/letter/j/77aa72/32.png) [@JRopers](https://discourse.itk.org/u/JRopers)
#### Post date: [July 8, 2023, 3:49pm UTC](https://discourse.itk.org/t/implementation-of-a-custom-imagetoimage-similarity-metric-for-registration/6047/5 "2023-07-08T15:49:57Z")

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Okay so the other problem with this is that I am trying to stack metrics using ObjectToObjectMultiMetricv4. Therefore the MatchCardinalityImageToImageMetric is not compatible. Maybe it helps explaining my overall idea.

I have the moving and fixed CT image. In addition to that, I have binary segmentation masks of various anatomies such as the lung for each image. I would like the registration to basically focus on both MSE between the two CTs, but also at the same time on the DICE metric between the masks I am passing and potentially even multiple masks of different anatomies, for which each DICE metric is weighted differently.  
I thought that if I successfully implement a DICE metric by inheriting from ImageToImageMetricv4, that I could stack and weight the different inputs, and their corresponding similarity metrics and weights, kind of like you explained [here](https://discourse.itk.org/t/similarity-measure/4680/8). Is my logic off? Is there no MatchCardinalityImageToImageMetricv4? If not, are there any resources on implementing your own metric in the v4 registration?

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<div class="post-metadata">

### Author: ![dzenanz](https://discourse.itk.org/user_avatar/discourse.itk.org/dzenanz/32/1093_2.png) [@dzenanz](https://discourse.itk.org/u/dzenanz)
#### Post date: [July 10, 2023, 3:07pm UTC](https://discourse.itk.org/t/implementation-of-a-custom-imagetoimage-similarity-metric-for-registration/6047/6 "2023-07-10T15:07:35Z")

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You can use [multi-metric](https://itk.org/Doxygen/html/classitk_1_1ObjectToObjectMultiMetricv4.html) to combine multiple metrics. For developing one’s own metric, there is no tutorial (as far as I know). Suggestion: pick the most similar existing metric, copy it, rename, and modify to suit your needs.

The resampling problem above: the default value is 0. For CT images, you might want -1000. For label images, you should use whatever value you use for background label. Hope this helps.