diff --git a/Modules/AlgorithmsExt/include/mitkUnstructuredGridClusteringFilter.h b/Modules/AlgorithmsExt/include/mitkUnstructuredGridClusteringFilter.h
index 743dae16d6..504db804c1 100644
--- a/Modules/AlgorithmsExt/include/mitkUnstructuredGridClusteringFilter.h
+++ b/Modules/AlgorithmsExt/include/mitkUnstructuredGridClusteringFilter.h
@@ -1,112 +1,140 @@
 /*===================================================================
 
 The Medical Imaging Interaction Toolkit (MITK)
 
 Copyright (c) German Cancer Research Center,
 Division of Medical and Biological Informatics.
 All rights reserved.
 
 This software is distributed WITHOUT ANY WARRANTY; without
 even the implied warranty of MERCHANTABILITY or FITNESS FOR
 A PARTICULAR PURPOSE.
 
 See LICENSE.txt or http://www.mitk.org for details.
 
 ===================================================================*/
 
 #ifndef _MITKUNSTRUCTUREDGRIDCLUSTERINGFILTER_h__
 #define _MITKUNSTRUCTUREDGRIDCLUSTERINGFILTER_h__
 
 #include <MitkAlgorithmsExtExports.h>
 
 #include <mitkCommon.h>
 
 #include <mitkUnstructuredGrid.h>
 #include <vtkSmartPointer.h>
 #include <mitkUnstructuredGridToUnstructuredGridFilter.h>
 #include <vtkIdList.h>
 #include <vtkPoints.h>
 
 
 namespace mitk {
 
    /**
-   * @brief The UnstructuredGridClusteringFilter class
+   * @brief This filter uses the DBSCAN algorithm for clustering an
+   * mitk::UnstructuredGrid. "MinPts" defines the number of neighbours which are
+   * required to be a kernel point if a point is in range of a kernel point
+   * but hasnt enough neighbours this point is added to the cluster but is a
+   * density reachable point and the cluster ends at this point. "eps" is the
+   * range in which the neighbours are searched. If "Meshing" is set the
+   * clusteres UnstructuredGrid is meshed and visible in 2D renderwindows.
    *
    * DBSCAN algorithm:
    *
    *     DBSCAN(D, eps, MinPts)
    *     C = 0
    *     for each unvisited point P in dataset D
    *       mark P as visited
    *       N = D.regionQuery(P, eps)
    *       if sizeof(N) < MinPts
    *         mark P as NOISE
    *       else
    *         C = next cluster
    *         expandCluster(P, N, C, eps, MinPts)
    *
    *     expandCluster(P, N, C, eps, MinPts)
    *       add P to cluster C
    *       for each point P' in N
    *         if P' is not visited
    *           mark P' as visited
    *           N' = D.regionQuery(P', eps)
    *           if sizeof(N') >= MinPts
    *             N = N joined with N'
    *         if P' is not yet member of any cluster
    *           add P' to cluster C
    */
 
   class MITKALGORITHMSEXT_EXPORT UnstructuredGridClusteringFilter
       : public UnstructuredGridToUnstructuredGridFilter
   {
     public:
 
       mitkClassMacro(UnstructuredGridClusteringFilter, UnstructuredGridToUnstructuredGridFilter)
       itkFactorylessNewMacro(Self)
       itkCloneMacro(Self)
 
+      /** Sets the distance for the neighbour search */
       itkSetMacro(eps, double)
       itkGetMacro(eps, double)
 
+      /** Sets the number of required neighbours */
       itkSetMacro(MinPts, int)
       itkGetMacro(MinPts, int)
 
+      /** If activated the clusteres UnstructuredGrid is meshed */
       itkSetMacro(Meshing, bool)
 
+      /** Returns all clusters as UnstructuredGrids which were found */
       virtual std::vector< mitk::UnstructuredGrid::Pointer> GetAllClusters();
 
+      /** Returns the number of the clusters which were found */
       virtual int GetNumberOfFoundClusters();
 
-      virtual void GenerateOutputInformation();
-
-      virtual void GenerateData();
-
     protected:
 
+      /** Constructor */
       UnstructuredGridClusteringFilter();
 
+      /** Destructor */
       virtual ~UnstructuredGridClusteringFilter();
 
+      /** Defines the output of the filter */
+      virtual void GenerateOutputInformation();
+
+      /** Is called by the Update() method */
+      virtual void GenerateData();
+
   private:
 
+      /** Used for the DBSCAN algorithm to expand a cluster and add more points to it */
       void ExpandCluster(int id, vtkIdList* pointIDs, vtkPoints* cluster, vtkPoints *inpPoints);
 
+      /** The result main Cluster */
       mitk::UnstructuredGrid::Pointer m_UnstructGrid;
 
+      /** All clusters which were found */
       std::vector< vtkSmartPointer<vtkPoints> > m_Clusters;
 
+      /** The distances of the points from the input UnstructuredGrid*/
+      std::vector< vtkSmartPointer<vtkDoubleArray> > m_DistanceArrays;
+
+      /** The range for the neighbout search */
       double m_eps;
 
+      /** The number of the required neighbours */
       int m_MinPts;
 
+      /** Activates the meshing for the UnstructuredGrid clusters*/
       bool m_Meshing;
 
+      /** If its activated the distance of the clusters is used instead of the
+      * size */
+      bool m_DistCalc;
+
   };
 
 } // namespace mitk
 
 #endif //_MITKUNSTRUCTUREDGRIDCLUSTERINGFILTER_h__
 
 
diff --git a/Modules/AlgorithmsExt/src/mitkUnstructuredGridClusteringFilter.cpp b/Modules/AlgorithmsExt/src/mitkUnstructuredGridClusteringFilter.cpp
index 9b1f089927..2a3ad2844d 100644
--- a/Modules/AlgorithmsExt/src/mitkUnstructuredGridClusteringFilter.cpp
+++ b/Modules/AlgorithmsExt/src/mitkUnstructuredGridClusteringFilter.cpp
@@ -1,218 +1,271 @@
 /*===================================================================
 
 The Medical Imaging Interaction Toolkit (MITK)
 
 Copyright (c) German Cancer Research Center,
 Division of Medical and Biological Informatics.
 All rights reserved.
 
 This software is distributed WITHOUT ANY WARRANTY; without
 even the implied warranty of MERCHANTABILITY or FITNESS FOR
 A PARTICULAR PURPOSE.
 
 See LICENSE.txt or http://www.mitk.org for details.
 
 ===================================================================*/
 
 #include <mitkUnstructuredGridClusteringFilter.h>
 
+#include <vector>
+
 #include <vtkSmartPointer.h>
 #include <vtkPointLocator.h>
 #include <vtkPoints.h>
 #include <vtkUnstructuredGrid.h>
 #include <vtkDelaunay3D.h>
 #include <vtkPolyVertex.h>
+#include <vtkPointData.h>
+#include <vtkDoubleArray.h>
+#include <vtkDataArray.h>
+#include <vtkVariant.h>
 
 
-mitk::UnstructuredGridClusteringFilter::UnstructuredGridClusteringFilter() : m_eps(0.0), m_MinPts(0), m_Meshing(true)
+mitk::UnstructuredGridClusteringFilter::UnstructuredGridClusteringFilter() : m_eps(5.0), m_MinPts(4), m_Meshing(false), m_DistCalc(false)
 {
   this->m_UnstructGrid = mitk::UnstructuredGrid::New();
 }
 
 mitk::UnstructuredGridClusteringFilter::~UnstructuredGridClusteringFilter(){}
 
 std::map<int, bool> visited;
 std::map<int, bool> isNoise;
 std::map<int, bool> clusterMember;
 vtkSmartPointer<vtkPointLocator> pLocator;
+std::vector< vtkSmartPointer<vtkPoints> > clusterVector;
+
+std::vector< std::vector<int> > clustersPointsIDs;
 
 void mitk::UnstructuredGridClusteringFilter::GenerateOutputInformation()
 {
   m_UnstructGrid = this->GetOutput();
 }
 
 void mitk::UnstructuredGridClusteringFilter::GenerateData()
 {
   mitk::UnstructuredGrid::Pointer inputGrid = const_cast<mitk::UnstructuredGrid*>(this->GetInput());
   if(inputGrid.IsNull()) return;
 
   vtkSmartPointer<vtkUnstructuredGrid> vtkInpGrid = inputGrid->GetVtkUnstructuredGrid();
   vtkSmartPointer<vtkPoints> inpPoints = vtkInpGrid->GetPoints();
   pLocator =vtkSmartPointer<vtkPointLocator>::New();
-  std::vector< vtkSmartPointer<vtkPoints> > clusterVector;
+
+  vtkSmartPointer<vtkDoubleArray> distances = vtkSmartPointer<vtkDoubleArray>::New();
+  if(inputGrid->GetVtkUnstructuredGrid()->GetPointData()->GetNumberOfArrays() > 0)
+  {
+    m_DistCalc = true;
+    distances = dynamic_cast<vtkDoubleArray*>(vtkInpGrid->GetPointData()->GetArray(0));
+  }
 
   pLocator->SetDataSet(vtkInpGrid);
   pLocator->AutomaticOn();
   pLocator->SetNumberOfPointsPerBucket(2);
   pLocator->BuildLocator();
 
   //fill the visited map with false for checking
   for(int i=0; i<inpPoints->GetNumberOfPoints();i++)
   {
     visited[i] = false;
     isNoise[i] = false;
     clusterMember[i] = false;
   }
 
   for(int i=0; i<inpPoints->GetNumberOfPoints();i++)
   {
     if(!visited[i])
     {
       visited[i] = true; //mark P as visited
       vtkSmartPointer<vtkIdList> idList = vtkSmartPointer<vtkIdList>::New(); //represent N
       pLocator->FindPointsWithinRadius(m_eps, inpPoints->GetPoint(i), idList); //N = D.regionQuery(P, eps)
       if(idList->GetNumberOfIds() < m_MinPts) //if sizeof(N) < MinPts
       {
         isNoise[i] = true; //mark P as NOISE
       }
       else
       {
         vtkSmartPointer<vtkPoints> cluster = vtkSmartPointer<vtkPoints>::New(); //represent a cluster
         clusterVector.push_back(cluster); //C = next cluster
         this->ExpandCluster(i,idList,cluster,inpPoints); //expandCluster(P, N, C, eps, MinPts) mod. the parameter list
       }
     }
   }
 
   //OUTPUT LOGIC
   m_Clusters = clusterVector;
   int numberOfClusterPoints = 0;
   int IdOfBiggestCluster = 0;
 
   for(unsigned int i=0; i<m_Clusters.size();i++)
   {
+    vtkSmartPointer<vtkDoubleArray> array = vtkSmartPointer<vtkDoubleArray>::New();
     vtkSmartPointer<vtkPoints> points = m_Clusters.at(i);
-    for(int j=0; j<points->GetNumberOfPoints();j++)
+    if(m_DistCalc)
     {
-      double point[3];
-      points->GetPoint(j,point);
+      array->SetNumberOfComponents(1);
+      array->SetNumberOfTuples(points->GetNumberOfPoints());
+      for(int j=0; j<points->GetNumberOfPoints();j++)
+      {
+        double point[3];
+        points->GetPoint(j,point);
+        if(clustersPointsIDs.at(i).at(j)<inpPoints->GetNumberOfPoints())
+        {
+          if(distances->GetValue(clustersPointsIDs.at(i).at(j)) > 0.001)
+          {
+            double dist[1] = {distances->GetValue(clustersPointsIDs.at(i).at(j))};
+            array->SetTuple(j, dist);
+          }
+          else
+          {
+            double dist[1] = {0.0};
+            array->SetTuple(j, dist);
+          }
+        }
+      }
+      m_DistanceArrays.push_back(array);
     }
     if(points->GetNumberOfPoints() > numberOfClusterPoints)
     {
       numberOfClusterPoints = points->GetNumberOfPoints();
       IdOfBiggestCluster = i;
     }
   }
 
   vtkSmartPointer<vtkUnstructuredGrid> biggestCluster = vtkSmartPointer<vtkUnstructuredGrid>::New();
 
   vtkSmartPointer<vtkPoints> points = vtkSmartPointer<vtkPoints>::New();
   points = m_Clusters.at(IdOfBiggestCluster);
 
   vtkSmartPointer<vtkPolyVertex> verts = vtkSmartPointer<vtkPolyVertex>::New();
   verts->GetPointIds()->SetNumberOfIds(m_Clusters.at(IdOfBiggestCluster)->GetNumberOfPoints());
   for(int i=0; i<m_Clusters.at(IdOfBiggestCluster)->GetNumberOfPoints(); i++)
   {
     verts->GetPointIds()->SetId(i,i);
   }
 
   biggestCluster->Allocate(1);
   biggestCluster->InsertNextCell(verts->GetCellType(), verts->GetPointIds());
   biggestCluster->SetPoints(m_Clusters.at(IdOfBiggestCluster));
 
   if(m_Meshing)
   {
     vtkSmartPointer<vtkDelaunay3D> mesher = vtkSmartPointer<vtkDelaunay3D>::New();
     mesher->SetInputData(biggestCluster);
     mesher->SetAlpha(0.9);
     mesher->Update();
 
     vtkSmartPointer<vtkUnstructuredGrid> output = mesher->GetOutput();
     m_UnstructGrid->SetVtkUnstructuredGrid(output);
   }
   else
   {
     m_UnstructGrid->SetVtkUnstructuredGrid(biggestCluster);
   }
+
+  clusterVector.clear();
+  clustersPointsIDs.clear();
 }
 
 void mitk::UnstructuredGridClusteringFilter::ExpandCluster(int id, vtkIdList *pointIDs, vtkPoints* cluster, vtkPoints* inpPoints)
 {
+  std::vector<int> x;
+  x.push_back(id);
   cluster->InsertNextPoint(inpPoints->GetPoint(id)); //add P to cluster C
-  clusterMember[id] = true; //right?
+  clusterMember[id] = true;
 
   vtkSmartPointer<vtkPoints> neighbours = vtkSmartPointer<vtkPoints>::New(); //same N as in other function
   inpPoints->GetPoints(pointIDs,neighbours);
 
   for(int i=0; i<pointIDs->GetNumberOfIds();i++) //for each point P' in N
   {
     if(!visited[pointIDs->GetId(i)]) //if P' is not visited
     {
       visited[pointIDs->GetId(i)] = true; //mark P' as visited
       vtkSmartPointer<vtkIdList> idList = vtkSmartPointer<vtkIdList>::New(); //represent N'
       pLocator->FindPointsWithinRadius(m_eps, inpPoints->GetPoint(pointIDs->GetId(i)), idList); //N' = D.regionQuery(P', eps)
+
       if(idList->GetNumberOfIds() >= m_MinPts) //if sizeof(N') >= MinPts
       {
         for(int j=0; j<idList->GetNumberOfIds();j++) //N = N joined with N'
         {
-          pointIDs->InsertNextId(idList->GetId(j));
+          if(idList->GetId(j)<inpPoints->GetNumberOfPoints())//a litte bit hacked ?!
+          {
+            pointIDs->InsertNextId(idList->GetId(j));
+          }
         }
       }
     }
     if(!clusterMember[pointIDs->GetId(i)]) //if P' is not yet member of any cluster
     {
-      clusterMember[pointIDs->GetId(i)] = true;
-      cluster->InsertNextPoint(inpPoints->GetPoint(pointIDs->GetId(i))); //add P' to cluster C
+      if(pointIDs->GetId(i)<inpPoints->GetNumberOfPoints()){
+        clusterMember[pointIDs->GetId(i)] = true;
+        x.push_back(pointIDs->GetId(i));
+        cluster->InsertNextPoint(inpPoints->GetPoint(pointIDs->GetId(i))); //add P' to cluster C
+      }
     }
   }
+
+  clustersPointsIDs.push_back(x);
 }
 
 std::vector<mitk::UnstructuredGrid::Pointer> mitk::UnstructuredGridClusteringFilter::GetAllClusters()
 {
   std::vector< mitk::UnstructuredGrid::Pointer > mitkUGridVector;
 
   for(unsigned int i=0; i<m_Clusters.size();i++)
   {
     vtkSmartPointer<vtkUnstructuredGrid> cluster = vtkSmartPointer<vtkUnstructuredGrid>::New();
 
     vtkSmartPointer<vtkPoints> points = m_Clusters.at(i);
 
     vtkSmartPointer<vtkPolyVertex> verts = vtkSmartPointer<vtkPolyVertex>::New();
 
     verts->GetPointIds()->SetNumberOfIds(points->GetNumberOfPoints());
-    for(int i=0; i<points->GetNumberOfPoints(); i++)
+    for(int j=0; j<points->GetNumberOfPoints(); j++)
     {
-      verts->GetPointIds()->SetId(i,i);
+      verts->GetPointIds()->SetId(j,j);
     }
 
     cluster->Allocate(1);
     cluster->InsertNextCell(verts->GetCellType(), verts->GetPointIds());
     cluster->SetPoints(points);
+    if(m_DistCalc)
+    {
+      cluster->GetPointData()->AddArray(m_DistanceArrays.at(i));
+    }
 
     mitk::UnstructuredGrid::Pointer mitkGrid = mitk::UnstructuredGrid::New();
 
     if(m_Meshing)
     {
       vtkSmartPointer<vtkDelaunay3D> mesher = vtkSmartPointer<vtkDelaunay3D>::New();
       mesher->SetInputData(cluster);
       mesher->SetAlpha(0.9);
       mesher->Update();
 
       vtkSmartPointer<vtkUnstructuredGrid> output = mesher->GetOutput();
       mitkGrid->SetVtkUnstructuredGrid(output);
     }
     else
     {
       mitkGrid->SetVtkUnstructuredGrid(cluster);
     }
 
     mitkUGridVector.push_back(mitkGrid);
   }
 
   return mitkUGridVector;
 }
 
 int mitk::UnstructuredGridClusteringFilter::GetNumberOfFoundClusters()
 {
   return m_Clusters.size();
 }
diff --git a/Modules/AlgorithmsExt/test/mitkUnstructuredGridClusteringFilterTest.cpp b/Modules/AlgorithmsExt/test/mitkUnstructuredGridClusteringFilterTest.cpp
index 8f2409f9e7..8adfc77384 100644
--- a/Modules/AlgorithmsExt/test/mitkUnstructuredGridClusteringFilterTest.cpp
+++ b/Modules/AlgorithmsExt/test/mitkUnstructuredGridClusteringFilterTest.cpp
@@ -1,129 +1,126 @@
 /*===================================================================
 
 The Medical Imaging Interaction Toolkit (MITK)
 
 Copyright (c) German Cancer Research Center,
 Division of Medical and Biological Informatics.
 All rights reserved.
 
 This software is distributed WITHOUT ANY WARRANTY; without
 even the implied warranty of MERCHANTABILITY or FITNESS FOR
 A PARTICULAR PURPOSE.
 
 See LICENSE.txt or http://www.mitk.org for details.
 
 ===================================================================*/
 
 #include "mitkTestingMacros.h"
 #include <mitkUnstructuredGridClusteringFilter.h>
 #include <mitkUnstructuredGridToUnstructuredGridFilter.h>
 #include <mitkTestFixture.h>
 #include <mitkUnstructuredGrid.h>
 
 #include <mitkIOUtil.h>
 
 #include <vtkSmartPointer.h>
 #include <vtkPoints.h>
 #include <vtkUnstructuredGrid.h>
-#include <vtkDebugLeaks.h>
 
 class mitkUnstructuredGridClusteringFilterTestSuite : public mitk::TestFixture
 {
   CPPUNIT_TEST_SUITE(mitkUnstructuredGridClusteringFilterTestSuite);
 
-  vtkDebugLeaks::SetExitError(0);
-
   MITK_TEST(testUnstructuredGridClusteringFilterInitialization);
   MITK_TEST(testInput);
   MITK_TEST(testUnstructuredGridGeneration);
   MITK_TEST(testReturnedCluster);
   MITK_TEST(testClusterVector);
   MITK_TEST(testGetNumberOfFoundClusters);
   CPPUNIT_TEST_SUITE_END();
 
 private:
 
   mitk::UnstructuredGrid::Pointer m_UnstructuredGrid;
 
 public:
 
   void setUp()
   {
     m_UnstructuredGrid = mitk::UnstructuredGrid::New();
 
     //Loading the test data
     std::vector< mitk::BaseData::Pointer > vector = mitk::IOUtil::Load(GetTestDataFilePath("UnstructuredGrid/scoredGrid.vtu"));
     mitk::BaseData::Pointer base = vector.at(0);
     m_UnstructuredGrid = dynamic_cast<mitk::UnstructuredGrid*>(base.GetPointer());
   }
 
   void testUnstructuredGridClusteringFilterInitialization()
   {
     mitk::UnstructuredGridClusteringFilter::Pointer clusterFilter = mitk::UnstructuredGridClusteringFilter::New();
     CPPUNIT_ASSERT_MESSAGE("Testing instantiation of filter object", clusterFilter.IsNotNull());
   }
 
   void testInput()
   {
     mitk::UnstructuredGridClusteringFilter::Pointer clusterFilter = mitk::UnstructuredGridClusteringFilter::New();
     clusterFilter->SetInput(m_UnstructuredGrid);
     CPPUNIT_ASSERT_MESSAGE("Testing set / get input!", clusterFilter->GetInput() == m_UnstructuredGrid);
   }
 
   void testUnstructuredGridGeneration()
   {
     mitk::UnstructuredGridClusteringFilter::Pointer clusterFilter = mitk::UnstructuredGridClusteringFilter::New();
     clusterFilter->SetInput(m_UnstructuredGrid);
     clusterFilter->SetMeshing(false);
     clusterFilter->SetMinPts(4);
     clusterFilter->Seteps(1.2);
     clusterFilter->Update();
     CPPUNIT_ASSERT_MESSAGE("Testing output generation!", clusterFilter->GetOutput() != NULL);
   }
 
   void testReturnedCluster()
   {
     mitk::UnstructuredGridClusteringFilter::Pointer clusterFilter = mitk::UnstructuredGridClusteringFilter::New();
     clusterFilter->SetInput(m_UnstructuredGrid);
     clusterFilter->SetMeshing(false);
     clusterFilter->SetMinPts(4);
     clusterFilter->Seteps(1.2);
     clusterFilter->Update();
     mitk::UnstructuredGrid::Pointer cluster = clusterFilter->GetOutput();
     CPPUNIT_ASSERT_MESSAGE("Testing the output cluster!", cluster->GetVtkUnstructuredGrid()->GetPoints()->GetNumberOfPoints() == 620);
   }
 
   void testClusterVector()
   {
     mitk::UnstructuredGridClusteringFilter::Pointer clusterFilter = mitk::UnstructuredGridClusteringFilter::New();
     clusterFilter->SetInput(m_UnstructuredGrid);
     clusterFilter->SetMeshing(false);
     clusterFilter->SetMinPts(4);
     clusterFilter->Seteps(1.2);
     clusterFilter->Update();
     std::vector< mitk::UnstructuredGrid::Pointer > clustervector = clusterFilter->GetAllClusters();
     //test that all clusters have points:
     bool havePoints = true;
     for(unsigned int i=0; i<clustervector.size(); i++)
     {
       mitk::UnstructuredGrid::Pointer grid = clustervector.at(i);
       if(grid->GetVtkUnstructuredGrid()->GetPoints()->GetNumberOfPoints()<1)
         havePoints = false;
     }
     CPPUNIT_ASSERT_MESSAGE("Testing number of found clusters!", havePoints && clustervector.size() == 17);
   }
 
   void testGetNumberOfFoundClusters()
   {
     mitk::UnstructuredGridClusteringFilter::Pointer clusterFilter = mitk::UnstructuredGridClusteringFilter::New();
     clusterFilter->SetInput(m_UnstructuredGrid);
     clusterFilter->SetMeshing(false);
     clusterFilter->SetMinPts(4);
     clusterFilter->Seteps(1.2);
     clusterFilter->Update();
     CPPUNIT_ASSERT_MESSAGE("Testing number of found clusters!", clusterFilter->GetNumberOfFoundClusters() == 17);
   }
 
 };
 
 MITK_TEST_SUITE_REGISTRATION(mitkUnstructuredGridClusteringFilter)
diff --git a/Modules/SurfaceInterpolation/files.cmake b/Modules/SurfaceInterpolation/files.cmake
index c162e1a981..40b96eee8e 100644
--- a/Modules/SurfaceInterpolation/files.cmake
+++ b/Modules/SurfaceInterpolation/files.cmake
@@ -1,8 +1,9 @@
 set(CPP_FILES
+  mitkClusteredPlaneSuggestionFilter.cpp
   mitkComputeContourSetNormalsFilter.cpp
   mitkCreateDistanceImageFromSurfaceFilter.cpp
   mitkPointCloudScoringFilter.cpp
   mitkImageToPointCloudFilter.cpp
   mitkReduceContourSetFilter.cpp
   mitkSurfaceInterpolationController.cpp
 )
diff --git a/Modules/SurfaceInterpolation/mitkClusteredPlaneSuggestionFilter.cpp b/Modules/SurfaceInterpolation/mitkClusteredPlaneSuggestionFilter.cpp
new file mode 100644
index 0000000000..4239061337
--- /dev/null
+++ b/Modules/SurfaceInterpolation/mitkClusteredPlaneSuggestionFilter.cpp
@@ -0,0 +1,164 @@
+/*===================================================================
+
+The Medical Imaging Interaction Toolkit (MITK)
+
+Copyright (c) German Cancer Research Center,
+Division of Medical and Biological Informatics.
+All rights reserved.
+
+This software is distributed WITHOUT ANY WARRANTY; without
+even the implied warranty of MERCHANTABILITY or FITNESS FOR
+A PARTICULAR PURPOSE.
+
+See LICENSE.txt or http://www.mitk.org for details.
+
+===================================================================*/
+
+#include <mitkClusteredPlaneSuggestionFilter.h>
+
+#include <deque>
+#include <fstream>
+#include <iostream>
+
+#include <vtkSmartPointer.h>
+#include <vtkUnstructuredGrid.h>
+#include <vtkPointLocator.h>
+#include <vtkPolyVertex.h>
+#include <vtkPointData.h>
+#include <vtkDoubleArray.h>
+
+#include <mitkPlaneFit.h>
+#include <mitkPointSet.h>
+#include <mitkUnstructuredGridClusteringFilter.h>
+
+
+mitk::ClusteredPlaneSuggestionFilter::ClusteredPlaneSuggestionFilter(): m_Meshing(false), m_MinPts(4), m_Eps(1.2), m_UseDistances(false), m_NumberOfUsedClusters(3)
+{
+  this->m_MainCluster = mitk::UnstructuredGrid::New();
+  this->m_GeoData = mitk::GeometryData::New();
+}
+
+mitk::ClusteredPlaneSuggestionFilter::~ClusteredPlaneSuggestionFilter(){}
+
+bool myComparison(std::pair<double,int> i, std::pair<double, int> j){ return (i.first>j.first); }
+
+void mitk::ClusteredPlaneSuggestionFilter::GenerateData()
+{
+  mitk::UnstructuredGrid::Pointer inpGrid = const_cast<mitk::UnstructuredGrid*>(this->GetInput());
+
+  if(inpGrid.IsNull())
+  {
+    MITK_ERROR << "Input or cast to UnstructuredGrid is null";
+    return;
+  }
+
+  mitk::UnstructuredGridClusteringFilter::Pointer clusterFilter = mitk::UnstructuredGridClusteringFilter::New();
+  clusterFilter->SetInput(inpGrid);
+  clusterFilter->SetMeshing(false);
+  clusterFilter->SetMinPts(m_MinPts);
+  clusterFilter->Seteps(m_Eps);
+  clusterFilter->Update();
+
+  vtkSmartPointer< vtkUnstructuredGrid > vtkGrid = clusterFilter->GetOutput()->GetVtkUnstructuredGrid();
+
+  if(!vtkGrid)
+  {
+    MITK_ERROR << "vtkUnstructuredGrid output from clustering is null";
+    return;
+  }
+
+  m_MainCluster->SetVtkUnstructuredGrid(vtkGrid);
+  m_Clusters = clusterFilter->GetAllClusters();
+
+  //find the avg distances of every cluster maybe a square mean for better looking at high values?
+  std::vector< std::pair<double/*value*/,int/*key/id*/> > avgDistances;
+
+  //get the number of points and the id of every cluster
+  std::vector< std::pair<int/*value*/,int/*key/id*/> > sizeIDs;
+
+  for(unsigned int i=0; i<m_Clusters.size();i++)
+  {
+      mitk::UnstructuredGrid::Pointer cluster = m_Clusters.at(i);
+      vtkSmartPointer<vtkDoubleArray> data = dynamic_cast<vtkDoubleArray*>(cluster->GetVtkUnstructuredGrid()->GetPointData()->GetArray(0));
+      double avg = 0.0;
+      for(int j=0; j<data->GetSize();j++)
+      {
+          avg += data->GetValue(j);
+      }
+      avgDistances.push_back(std::make_pair(avg/static_cast<double>(data->GetSize()),i));
+  }
+  //now sort the clusters with their distances
+
+  for(unsigned int i=0; i<m_Clusters.size();i++)
+  {
+    sizeIDs.push_back(std::make_pair( m_Clusters.at(i)->GetVtkUnstructuredGrid()->GetNumberOfPoints(), i));
+  }
+
+  //sort the point IDs for finding the biggest clusters and clusters with the
+  //highest distance
+  //sizeIDs is sorted by number of points in each cluster
+  //avgDistances is sorted by avg distance to next edge
+  std::sort(sizeIDs.begin(), sizeIDs.end(), myComparison);
+  std::sort(avgDistances.begin(), avgDistances.end(), myComparison);
+
+  int number = 0; //max number of biggest clusters which are used for the plane
+
+  //if less than m_NumberOfUsedClusters clusters are found
+  if(m_Clusters.size() < m_NumberOfUsedClusters)
+    number = m_Clusters.size();
+  else
+    number = m_NumberOfUsedClusters;
+
+  //Generate a pointset from UnstructuredGrid for the PlaneFitFilter:
+  mitk::PointSet::Pointer pointset = mitk::PointSet::New();
+
+  int pointId = 0;
+
+  for(int j=0; j<number; j++) //iterate over the three(or less) biggest clusters
+  {
+    //Get the cluster with "id" from all clusters, "id" is saved in sizeIDs.second
+    //sizeIDs.first represent the number of points which is only needed for sorting the "id"s
+    vtkSmartPointer<vtkUnstructuredGrid> tmpGrid;
+
+    if(m_UseDistances)
+      tmpGrid = m_Clusters.at(avgDistances.at(j).second)->GetVtkUnstructuredGrid(); //highest distance
+    else
+      tmpGrid = m_Clusters.at(sizeIDs.at(j).second)->GetVtkUnstructuredGrid(); //biggest cluster
+
+    for(int i=0; i<tmpGrid->GetNumberOfPoints();i++)
+    {
+      mitk::Point3D point;
+      point[0] = tmpGrid->GetPoint(i)[0];
+      point[1] = tmpGrid->GetPoint(i)[1];
+      point[2] = tmpGrid->GetPoint(i)[2];
+
+      pointset->InsertPoint(pointId,point);
+      pointId++;
+    }
+  }
+
+  mitk::PlaneFit::Pointer planeFilter = mitk::PlaneFit::New();
+  planeFilter->SetInput(pointset);
+  planeFilter->Update();
+
+  m_GeoData = planeFilter->GetOutput();
+
+  if(m_GeoData.IsNull())
+  {
+    MITK_ERROR << "GeometryData output from PlaneFit filter is null";
+    return;
+  }
+
+  avgDistances.clear();
+}
+
+void mitk::ClusteredPlaneSuggestionFilter::GenerateOutputInformation()
+{
+  mitk::UnstructuredGrid::ConstPointer inputImage = this->GetInput();
+
+  m_MainCluster = this->GetOutput();
+
+  itkDebugMacro(<<"GenerateOutputInformation()");
+
+  if(inputImage.IsNull()) return;
+}
diff --git a/Modules/SurfaceInterpolation/mitkClusteredPlaneSuggestionFilter.h b/Modules/SurfaceInterpolation/mitkClusteredPlaneSuggestionFilter.h
new file mode 100644
index 0000000000..83c9b00440
--- /dev/null
+++ b/Modules/SurfaceInterpolation/mitkClusteredPlaneSuggestionFilter.h
@@ -0,0 +1,127 @@
+/*===================================================================
+
+The Medical Imaging Interaction Toolkit (MITK)
+
+Copyright (c) German Cancer Research Center,
+Division of Medical and Biological Informatics.
+All rights reserved.
+
+This software is distributed WITHOUT ANY WARRANTY; without
+even the implied warranty of MERCHANTABILITY or FITNESS FOR
+A PARTICULAR PURPOSE.
+
+See LICENSE.txt or http://www.mitk.org for details.
+
+===================================================================*/
+
+#ifndef _MITKCLUSTEREDPLANESUGGESTIONFILTER_h__
+#define _MITKCLUSTEREDPLANESUGGESTIONFILTER_h__
+
+#include <MitkSurfaceInterpolationExports.h>
+
+#include <mitkGeometryData.h>
+#include <mitkUnstructuredGridToUnstructuredGridFilter.h>
+
+
+namespace mitk {
+
+   /**
+   * @brief Clustering an UnstructuredGrid and calculating a Plane through it.
+   *
+   * The output is the biggest found cluster but you can get all clusters in a
+   * std::vector represented by vtkPoints. Use GetClusters() the get the vector.
+   * With GetGeoData() you get the calculated geometry as a mitk::GeometryData.
+   * Internally the mitk::UnstructuredGridClusteringFilter is used for
+   * clustering and after the mitk::PlaneFit for calculating the plane.
+   * The parameters m_Meshing (Set/GetMeshing()), m_MinPts (Set/GetMinPts()) and
+   * m_Eps (Set/GetEps()) are used for the UnstructuredGridClusteringFilter.
+   */
+  class MITKSURFACEINTERPOLATION_EXPORT ClusteredPlaneSuggestionFilter
+      : public UnstructuredGridToUnstructuredGridFilter
+  {
+    public:
+
+      mitkClassMacro(ClusteredPlaneSuggestionFilter,
+                     UnstructuredGridToUnstructuredGridFilter)
+
+      itkFactorylessNewMacro(Self)
+
+      itkCloneMacro(Self)
+
+      /** Returns the geometry of the calculated plane from mitk::PlaneFit */
+      itkGetMacro(GeoData, mitk::GeometryData::Pointer)
+
+      /** Returns all clusters which were found by the clustering filter */
+      itkGetMacro(Clusters, std::vector< mitk::UnstructuredGrid::Pointer >)
+
+      /** Activate the meshing function for the returned clusters. The meshing
+      * is needed to see the result in the 2D-renderwindows */
+      itkGetMacro(Meshing, bool)
+      itkSetMacro(Meshing, bool)
+
+      /** Minimal points which have to be located in the neighbourhood to define
+      * the point as a core point. For more information see DBSCAN algorithm */
+      itkGetMacro(MinPts, int)
+      itkSetMacro(MinPts, int)
+
+      /** The range/neighbourhood for clustering the points. For more
+      * information see DBSCAN algorithm */
+      itkGetMacro(Eps, double)
+      itkSetMacro(Eps, double)
+
+      /** Setting to true, uses the distances of the clusters otherwise the
+      * the size of the clusters is used */
+      itkSetMacro(UseDistances, bool)
+
+      /** Sets the number of clusters which are used for the plane suggestion
+      * if the number of found clusters is lower than the "NumberOfUsedClusters"
+      * all found clusters are used */
+      itkSetMacro(NumberOfUsedClusters, int)
+
+
+    protected:
+
+      /** Constructor */
+      ClusteredPlaneSuggestionFilter();
+
+      /** Destructor */
+      virtual ~ClusteredPlaneSuggestionFilter();
+
+      /** Is called by the Update() method of the filter */
+      virtual void GenerateData();
+
+      /** Defines the output of the filter */
+      virtual void GenerateOutputInformation();
+
+
+  private:
+
+      /** The geometry of the calculated plane */
+      mitk::GeometryData::Pointer m_GeoData;
+
+      /** The vector which holds all found clusters */
+      std::vector< mitk::UnstructuredGrid::Pointer > m_Clusters;
+
+      /** The biggest found cluster - the output */
+      mitk::UnstructuredGrid::Pointer m_MainCluster;
+
+      /** Connect the points to meshes. Required for 2D rendering */
+      bool m_Meshing;
+
+      /** Number of points required to define a core point */
+      int m_MinPts;
+
+      /** The distance/neighbourhood for clustering */
+      double m_Eps;
+
+      /** Decides to use the distances or the size */
+      bool m_UseDistances;
+
+      /** The number of clusters which are used for the plane suggestion */
+      unsigned int m_NumberOfUsedClusters;
+
+  };
+
+} // namespace mitk
+
+#endif //_MITKCLUSTEREDPLANESUGGESTIONFILTER_h__