Medical Imaging Interaction Toolkit  2018.4.99-389bf124
Medical Imaging Interaction Toolkit
mitkGIFNeighbouringGreyLevelDependenceFeatureTest.cpp
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1 /*============================================================================
2 
3 The Medical Imaging Interaction Toolkit (MITK)
4 
5 Copyright (c) German Cancer Research Center (DKFZ)
6 All rights reserved.
7 
8 Use of this source code is governed by a 3-clause BSD license that can be
9 found in the LICENSE file.
10 
11 ============================================================================*/
12 
13 #include <mitkTestingMacros.h>
14 #include <mitkTestFixture.h>
15 #include "mitkIOUtil.h"
16 #include <cmath>
17 
18 #include <mitkGIFNeighbouringGreyLevelDependenceFeatures.h>
19 
20 class mitkGIFNeighbouringGreyLevelDependenceFeatureTestSuite : public mitk::TestFixture
21 {
22  CPPUNIT_TEST_SUITE(mitkGIFNeighbouringGreyLevelDependenceFeatureTestSuite );
23 
24  MITK_TEST(ImageDescription_PhantomTest_3D);
25  MITK_TEST(ImageDescription_PhantomTest_2D);
26 
27  CPPUNIT_TEST_SUITE_END();
28 
29 private:
30  mitk::Image::Pointer m_IBSI_Phantom_Image_Small;
31  mitk::Image::Pointer m_IBSI_Phantom_Image_Large;
32  mitk::Image::Pointer m_IBSI_Phantom_Mask_Small;
33  mitk::Image::Pointer m_IBSI_Phantom_Mask_Large;
34 
35 public:
36 
37  void setUp(void) override
38  {
39  m_IBSI_Phantom_Image_Small = mitk::IOUtil::Load<mitk::Image>(GetTestDataFilePath("Radiomics/IBSI_Phantom_Image_Small.nrrd"));
40  m_IBSI_Phantom_Image_Large = mitk::IOUtil::Load<mitk::Image>(GetTestDataFilePath("Radiomics/IBSI_Phantom_Image_Large.nrrd"));
41  m_IBSI_Phantom_Mask_Small = mitk::IOUtil::Load<mitk::Image>(GetTestDataFilePath("Radiomics/IBSI_Phantom_Mask_Small.nrrd"));
42  m_IBSI_Phantom_Mask_Large = mitk::IOUtil::Load<mitk::Image>(GetTestDataFilePath("Radiomics/IBSI_Phantom_Mask_Large.nrrd"));
43  }
44 
45  void ImageDescription_PhantomTest_3D()
46  {
47  mitk::GIFNeighbouringGreyLevelDependenceFeature::Pointer featureCalculator = mitk::GIFNeighbouringGreyLevelDependenceFeature::New();
48 
49  featureCalculator->SetUseBinsize(true);
50  featureCalculator->SetBinsize(1.0);
51  featureCalculator->SetUseMinimumIntensity(true);
52  featureCalculator->SetUseMaximumIntensity(true);
53  featureCalculator->SetMinimumIntensity(0.5);
54  featureCalculator->SetMaximumIntensity(6.5);
55 
56  auto featureList = featureCalculator->CalculateFeatures(m_IBSI_Phantom_Image_Large, m_IBSI_Phantom_Mask_Large);
57 
58  std::map<std::string, double> results;
59  for (auto valuePair : featureList)
60  {
61  MITK_INFO << valuePair.first << " : " << valuePair.second;
62  results[valuePair.first] = valuePair.second;
63  }
64  CPPUNIT_ASSERT_EQUAL_MESSAGE("Image Diagnostics should calculate 24 features.", std::size_t(24), featureList.size());
65 
66  // These values are obtained with IBSI
67  // Standard accuracy is 0.01
68  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Low Dependence Emphasis with Large IBSI Phantom Image", 0.045, results["Neighbouring Grey Level Dependence::Low Dependence Emphasis"], 0.001);
69  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::High Dependence Emphasis with Large IBSI Phantom Image", 109, results["Neighbouring Grey Level Dependence::High Dependence Emphasis"], 1.0);
70  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Low Grey Level Count Emphasis with Large IBSI Phantom Image", 0.693, results["Neighbouring Grey Level Dependence::Low Grey Level Count Emphasis"], 0.01);
71  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::High Grey Level Count Emphasis with Large IBSI Phantom Image", 7.66, results["Neighbouring Grey Level Dependence::High Grey Level Count Emphasis"], 0.01);
72  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Low Dependence Low Grey Level Emphasis with Large IBSI Phantom Image", 0.00963, results["Neighbouring Grey Level Dependence::Low Dependence Low Grey Level Emphasis"], 0.001);
73  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Low Dependence High Grey Level Emphasis with Large IBSI Phantom Image", 0.736, results["Neighbouring Grey Level Dependence::Low Dependence High Grey Level Emphasis"], 0.01);
74  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::High Dependence Low Grey Level Emphasis with Large IBSI Phantom Image", 102, results["Neighbouring Grey Level Dependence::High Dependence Low Grey Level Emphasis"], 1);
75  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::High Dependence High Grey Level Emphasis with Large IBSI Phantom Image", 235, results["Neighbouring Grey Level Dependence::High Dependence High Grey Level Emphasis"], 1);
76  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Grey Level Non-Uniformity with Large IBSI Phantom Image", 37.9, results["Neighbouring Grey Level Dependence::Grey Level Non-Uniformity"], 0.1);
77  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Grey Level Non-Uniformity Normalised with Large IBSI Phantom Image", 0.512, results["Neighbouring Grey Level Dependence::Grey Level Non-Uniformity Normalised"], 0.01);
78  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Dependence Count Non-Uniformity with Large IBSI Phantom Image", 4.86, results["Neighbouring Grey Level Dependence::Dependence Count Non-Uniformity"], 0.01);
79  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Dependence Count Non-Uniformity Normalised with Large IBSI Phantom Image", 0.0657, results["Neighbouring Grey Level Dependence::Dependence Count Non-Uniformity Normalised"], 0.001);
80  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Dependence Count Percentage with Large IBSI Phantom Image", 1, results["Neighbouring Grey Level Dependence::Dependence Count Percentage"], 0.01);
81  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Grey Level Variance with Large IBSI Phantom Image", 3.05, results["Neighbouring Grey Level Dependence::Grey Level Variance"], 0.01);
82  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Dependence Count Variance with Large IBSI Phantom Image", 22.1, results["Neighbouring Grey Level Dependence::Dependence Count Variance"], 0.1);
83  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Dependence Count Entropy with Large IBSI Phantom Image", 4.4, results["Neighbouring Grey Level Dependence::Dependence Count Entropy"], 0.01);
84  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Dependence Count Energy with Large IBSI Phantom Image", 0.0533, results["Neighbouring Grey Level Dependence::Dependence Count Energy"], 0.01);
85 
86  // These values are obtained by manually running the tool
87  // Values might be wrong.
88  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Grey Level Mean with Large IBSI Phantom Image", 2.15, results["Neighbouring Grey Level Dependence::Grey Level Mean"], 0.01);
89  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Dependence Count Mean with Large IBSI Phantom Image", 9.32, results["Neighbouring Grey Level Dependence::Dependence Count Mean"], 0.1);
90  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Expected Neighbourhood Size with Large IBSI Phantom Image", 26, results["Neighbouring Grey Level Dependence::Expected Neighbourhood Size"], 0.01);
91  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Average Neighbourhood Size with Large IBSI Phantom Image", 14.24, results["Neighbouring Grey Level Dependence::Average Neighbourhood Size"], 0.01);
92  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Average Incomplete Neighbourhood Size with Large IBSI Phantom Image", 14.24, results["Neighbouring Grey Level Dependence::Average Incomplete Neighbourhood Size"], 0.01);
93  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Percentage of complete Neighbourhoods with Large IBSI Phantom Image", 0, results["Neighbouring Grey Level Dependence::Percentage of complete Neighbourhoods"], 0.01);
94  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Percentage of Dependence Neighbour Voxels with Large IBSI Phantom Image", 0.584, results["Neighbouring Grey Level Dependence::Percentage of Dependence Neighbour Voxels"], 0.01);
95  }
96 
97  void ImageDescription_PhantomTest_2D()
98  {
99  mitk::GIFNeighbouringGreyLevelDependenceFeature::Pointer featureCalculator = mitk::GIFNeighbouringGreyLevelDependenceFeature::New();
100 
101  featureCalculator->SetUseBinsize(true);
102  featureCalculator->SetBinsize(1.0);
103  featureCalculator->SetUseMinimumIntensity(true);
104  featureCalculator->SetUseMaximumIntensity(true);
105  featureCalculator->SetMinimumIntensity(0.5);
106  featureCalculator->SetMaximumIntensity(6.5);
107 
108  auto featureList = featureCalculator->CalculateFeaturesSlicewise(m_IBSI_Phantom_Image_Large, m_IBSI_Phantom_Mask_Large, 2);
109 
110  std::map<std::string, double> results;
111  for (auto valuePair : featureList)
112  {
113  MITK_INFO << valuePair.first << " : " << valuePair.second;
114  results[valuePair.first] = valuePair.second;
115  }
116  CPPUNIT_ASSERT_EQUAL_MESSAGE("Image Diagnostics should calculate 144 features.", std::size_t(144), featureList.size());
117 
118  // These values are obtained with IBSI
119  // Standard accuracy is 0.01
120  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Low Dependence Emphasis with Large IBSI Phantom Image", 0.158, results["SliceWise Mean Neighbouring Grey Level Dependence::Low Dependence Emphasis"], 0.001);
121  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::High Dependence Emphasis with Large IBSI Phantom Image", 19.2, results["SliceWise Mean Neighbouring Grey Level Dependence::High Dependence Emphasis"], 1.0);
122  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Low Grey Level Count Emphasis with Large IBSI Phantom Image", 0.702, results["SliceWise Mean Neighbouring Grey Level Dependence::Low Grey Level Count Emphasis"], 0.01);
123  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::High Grey Level Count Emphasis with Large IBSI Phantom Image", 7.49, results["SliceWise Mean Neighbouring Grey Level Dependence::High Grey Level Count Emphasis"], 0.01);
124  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Low Dependence Low Grey Level Emphasis with Large IBSI Phantom Image", 0.0473, results["SliceWise Mean Neighbouring Grey Level Dependence::Low Dependence Low Grey Level Emphasis"], 0.001);
125  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Low Dependence High Grey Level Emphasis with Large IBSI Phantom Image", 3.06, results["SliceWise Mean Neighbouring Grey Level Dependence::Low Dependence High Grey Level Emphasis"], 0.01);
126  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::High Dependence Low Grey Level Emphasis with Large IBSI Phantom Image", 17.6, results["SliceWise Mean Neighbouring Grey Level Dependence::High Dependence Low Grey Level Emphasis"], 1);
127  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::High Dependence High Grey Level Emphasis with Large IBSI Phantom Image", 49.5, results["SliceWise Mean Neighbouring Grey Level Dependence::High Dependence High Grey Level Emphasis"], 1);
128  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Grey Level Non-Uniformity with Large IBSI Phantom Image", 10.2, results["SliceWise Mean Neighbouring Grey Level Dependence::Grey Level Non-Uniformity"], 0.1);
129  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Grey Level Non-Uniformity Normalised with Large IBSI Phantom Image", 0.562, results["SliceWise Mean Neighbouring Grey Level Dependence::Grey Level Non-Uniformity Normalised"], 0.01);
130  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Dependence Count Non-Uniformity with Large IBSI Phantom Image", 3.96, results["SliceWise Mean Neighbouring Grey Level Dependence::Dependence Count Non-Uniformity"], 0.01);
131  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Dependence Count Non-Uniformity Normalised with Large IBSI Phantom Image", 0.212, results["SliceWise Mean Neighbouring Grey Level Dependence::Dependence Count Non-Uniformity Normalised"], 0.001);
132  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Dependence Count Percentage with Large IBSI Phantom Image", 1, results["SliceWise Mean Neighbouring Grey Level Dependence::Dependence Count Percentage"], 0.01);
133  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Grey Level Variance with Large IBSI Phantom Image", 2.7, results["SliceWise Mean Neighbouring Grey Level Dependence::Grey Level Variance"], 0.01);
134  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Dependence Count Variance with Large IBSI Phantom Image", 2.73, results["SliceWise Mean Neighbouring Grey Level Dependence::Dependence Count Variance"], 0.1);
135  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Dependence Count Entropy with Large IBSI Phantom Image", 2.71, results["SliceWise Mean Neighbouring Grey Level Dependence::Dependence Count Entropy"], 0.01);
136  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Dependence Count Energy with Large IBSI Phantom Image", 0.17, results["SliceWise Mean Neighbouring Grey Level Dependence::Dependence Count Energy"], 0.01);
137 
138  // These values are obtained by manually running the tool
139  // Values might be wrong.
140  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Grey Level Mean with Large IBSI Phantom Image", 2.12, results["SliceWise Mean Neighbouring Grey Level Dependence::Grey Level Mean"], 0.01);
141  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Dependence Count Mean with Large IBSI Phantom Image", 3.98, results["SliceWise Mean Neighbouring Grey Level Dependence::Dependence Count Mean"], 0.1);
142  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Expected Neighbourhood Size with Large IBSI Phantom Image", 8, results["SliceWise Mean Neighbouring Grey Level Dependence::Expected Neighbourhood Size"], 0.01);
143  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Average Neighbourhood Size with Large IBSI Phantom Image", 5.20, results["SliceWise Mean Neighbouring Grey Level Dependence::Average Neighbourhood Size"], 0.01);
144  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Average Incomplete Neighbourhood Size with Large IBSI Phantom Image", 4.5598, results["SliceWise Mean Neighbouring Grey Level Dependence::Average Incomplete Neighbourhood Size"], 0.01);
145  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Percentage of complete Neighbourhoods with Large IBSI Phantom Image", 0.1831, results["SliceWise Mean Neighbouring Grey Level Dependence::Percentage of complete Neighbourhoods"], 0.01);
146  CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Percentage of Dependence Neighbour Voxels with Large IBSI Phantom Image", 0.579, results["SliceWise Mean Neighbouring Grey Level Dependence::Percentage of Dependence Neighbour Voxels"], 0.01);
147  }
148 
149 };
150 
151 MITK_TEST_SUITE_REGISTRATION(mitkGIFNeighbouringGreyLevelDependenceFeature )
MITK_TEST_SUITE_REGISTRATION
MITK_TEST_SUITE_REGISTRATION(mitkImageToItk)
MITK_INFO
#define MITK_INFO
Definition: mitkLogMacros.h:18
MITK_TEST
#define MITK_TEST(TESTMETHOD)
Adds a test to the current test suite.
Definition: mitkTestingMacros.h:295
mitk::TestFixture::GetTestDataFilePath
static std::string GetTestDataFilePath(const std::string &testData)
Get the absolute path for test data.
Definition: mitkTestFixture.h:107
mitk::TestFixture
Test fixture for parameterized tests.
Definition: mitkTestFixture.h:86