1. Dr. Engr. Sami ur Rahman Assistant Professor Department of Computer Science University of Malakand Medical Imaging ITK

2. Credit Some of the slides are taken from the presenation of Jean-Loïc Rose Other contributers are cited at the end. University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 2

3. Installing ITK  Download InsightTookit-2.0.1.zip from http://www.itk.org/HTML/Download.htm http://www.itk.org/HTML/Download.htm  Download CMake installer from http://www.cmake.org/HTML/Download.html http://www.cmake.org/HTML/Download.html  Install CMake  Unzip InsightToolkit to folder  Run CMake set PATH\InsightToolkit-2.0.1 as source code folder  Set PATH\InsightToolkit-2.0.1-bin as where to build binaries  Disable BUILD_EXAMPLES and BUILD_TESTING  Click Configure button University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 3

4. Installing ITK (part 2)  Open ITK.dsw in binary build directory  Select Active Configuration to ALL_BUILD – Win32 RelWithDebInfo  Click Build  Wait 15-20 minutes University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 4

5. Build Sample Program  Unzip itkdemo.zip to some folder  Run CMake same as for ITK  For ITK_DIR set binary directory  Open itkdemp.dsw in Visual Studio  Itkdemo.exe University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 5

6. Contents  What is ITK  Background  Concepts  Data representations  Images and regions  Pixel access in ITK  Developer’s guide  Access pixel data  How to write a filter University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 6

7. ITK Basics  Image Processing and Analysis Toolkit  No visualization (VTK recommended)  Does not include GUI framework  Designed for Medical Imaging Applications  In general algorithms work in N-dimensions University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 7

8. Catalog of ITK Features  Image IO  Image processing  Canny Edge  Hough Transform (lines/ellipsoids)  Variable Conductance Diffusion  Geometry IO/representation/processing (Spatial Objects)  Statistics  Registration/Segmentation  Numerics  Optimizers  Finite Element Simulation University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 8

9. How code using ITK is written  Mostly in C++  Heavily templated/generic programming like STL  Multi-threading capable  Pipeline architecture 1.Build Pipeline 2.Execute pipeline  CMake used as build system Reader Image File Gaussian Image Writer File University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 9

10.  http://www.itk.org/ItkSoftwareGuide.pdf http://www.itk.org/ItkSoftwareGuide.pdf  http://www.itk.org/Doxygen/html/index.html  Follow the link Alphabetical List  Follow the link Groups  Post to the insight-users mailing list How to find what you need ? University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 10

11. How to integrate ITK ITK Image Processing C++ Glue code Visualization OpenGL, VTK GUI MFC, QT, BBTK, wxWin University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 11

12. Concepts  Data Pipeline  Smart Pointers  C++ Generic Programming (Templates) University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 12

13. Concepts (I) Pipeline architecture Image Filter Image Filter  Data pipeline Image University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 13

14. Concepts (I) Pipeline architecture  Pipeline architecture 1.Build Pipeline 2.Execute pipeline Reader Image File Gaussian Image Writer File University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 14

15. Concepts (II) An aside: Smart pointer  In C++ you typically allocate memory with new and desallocate it with delete  Smart pointers get around this problem by allocating and deallocating memory for you  You do not explicitly delete objects in ITK, this occurs automatically when they go out of scope  Since you can’t forget to delete objects, you can’t leak memory memory leak Cat* pCat = new Cat; pCat->Meaow(); delete pCat; University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 15

16. … … … … … … … … … … Concepts (III) C++ generic programming  Generic programming is a method of organizing libraries consisting of generic—or reusable—  Abstraction of Types and Behaviors Example: STL Standard Template Library std::vector 1 1 0 0 5 5 4 4 3 3.02 2.5.23.47 5.6 … … … … … … … … … … … … … … … … … … … … University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 16

17. Concepts (III) C++ generic programming Template ? Pixel type (char, int, …) Dimension (2D, 3D, …) University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 17

18. Contents  What is ITK  Background  Concepts  Data representations  Images and regions  Pixel access in ITK  Developer’s guide  Access pixel data  How to write a filter ITK Features Data representations Filtering Applications University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 18

19. Data storage in ITK  ITK separates storage of data from the actions you can perform on data  The DataObject class is the base class for the major “containers” into which you can place data  Images: N-d rectilinear grids of regularly sampled data  Meshes: N-d collections of points linked together into cells (e.g. triangles) University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 19

20. Class hierarchy itk::DataObject itk::Object itk::ImageBase itk::Image itk::PointSet itk::Mesh University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 20

21. Images and regions  ITK was designed to allow analysis of very large images, even images that far exceed the available RAM of a computer  For this reason, ITK distinguishes between an entire image and the part which is actually resident in memory or requested by an algorithm University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 21

22. Images and regions Algorithms only process a region of an image that sits inside the current buffer  The BufferedRegion is the portion of image in physical memory  The RequestedRegion is the portion of image to be processed  The LargestPossibleRegion describes the entire dataset LargestPossibleRegion::Index BufferedRegion::Index RequestedRegion::Index RequestedRegion::Size BufferedRegion::Size LargestPossibleRegion::Size University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 22

23. Images are templated  Image ITK  Example Pixel type Dimensionality (value) Unsigned char, 2 itk::Image itk::Image, 2 > University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 23

24. Images and regions  The favorite keyword  Declaring an image type  We can now use ImageType in place of the full class name, a nice convenience  Remember that names ending in “Type” are types, not variables or class names typedef itk::Image ImageType; typedef University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 24

25. Images and regions  Creating an image pointer  An image is created by invoking the New() operator from the corresponding image type and assigning the result to a SmartPointer. Pointer is typedef in itk::Image Macro “big New” typedef itk::Image ImageType; ImageType::Pointer image = ImageType::New(); University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 25

26. Images and regions  Creating an image  Classes can have typedefs as members. In this case, SizeType is a public member of itk::Image. typedef itk::Image ImageType; ImageType::Pointer image = ImageType::New(); ImageType::SizeType size; size[0] = 512; // x direction size[1] = 512; // y direction ImageType::IndexType start; start[0] = 0; // x direction start[1] = 0; // y direction University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 26

27.  Creating the region of an image Images and regions typedef itk::Image ImageType; ImageType::Pointer image = ImageType::New(); ImageType::SizeType size = {{512,512}}; ImageType::IndexType start = {{0,0}}; // Initialize region parameter ImageType::RegionType region; region.SetSize( size ); region.SetIndex( start ); University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 27

28. typedef itk::Image ImageType; ImageType::Pointer image = ImageType::New(); ImageType::SizeType size = {{512,512}}; ImageType::IndexType start = {{0,0}}; // Initialize region parameter ImageType::RegionType region; region.SetSize( size ); region.SetIndex( start ); // Allocate image image->SetRegions( region ); image->Allocate( ); image->FillBuffer( 0 ); Images and regions  Allocate The SetRegions function sets all 3 regions to the same region and Allocate sets aside memory for the image. University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 28

29. Image features Data space vs. “physical” space Image IO University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 29

30. Data space vs. “physical” space  Data space is an N-d array with integer indices, indexed from 0 to (L i - 1)  e.g. pixel (3,0,5) in 3D space  Physical space relates to data space by defining the origin and spacing of the image University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 30

31. Data space vs. “physical” space  Spacing: We can specify spacing by calling the SetSpacing() function in Image.  Origin: Similarly, we can set the image origin ImageType::SpacingType spacing; spacing[0] = 0.83; // x direction spacing[1] = 2.15; // y direction Image->SetSpacing( spacing ); ImageType::IndexType origin; origin[0] = 0.83; // x direction origin[1] = 2.15; // y direction Image->SetIndex( origin ); University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 31

32. Data space vs. “physical” space  The image class contains a number of convenience methods to convert between pixel indices and physical positions (as stored in the Point class) typedef itk::Image ImageType; ImageType::PointType point; // Physical space ImageType::PixelType pixelIndex; // Data space image->TransformPhysicalPointToIndex( point, pixelIndex ); image->TransformIndexToPhysicalPoint( pixelIndex, point ); University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 32

33. ITK Image IO CustomImageIO Image File ImageFileReader Image PNGImageIO VTKImageIODICOMImageIOGIPLImageIO MetaImageIOAnalyzeImageIO Loadable Factories University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 33

34. ITK Image IO Image File ImageFileReader Image Filter Image File ImageFileWriter Image University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 34

35. ITK Image IO #include “itkImageFileReader.h” #include “itkImageFileWriter.h” int main() { typedef itk::Image ImageType; typedef itk::ImageFileReader ReaderType; ReaderType::Pointer reader = ReaderType::New(); reader->SetFilename( inputFilename ); reader->Update( ); // reader->Update( ); typedef itk::ImageFileWiter WriterType; WriterType::Pointer writer = WriterType::New(); writer->SetInput( reader->GetOutput( ) ); writer->SetFilename( outputFilename ); writer->Update( ); } University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 35

36. Image typedef  Classes can have typedefs as members. typedef itk::Image ImageType; ImageType::Pointer // Image pointer ImageType::SizeType // Size of image ImageType::IndexType // Index of pixels ImageType::PixelType // Type of pixels ImageType::RegionType // region of pixels ImageType::… University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 36

37. Contents  What is ITK  Background  Concepts  Data representations  Images and regions  Pixel access in ITK  Developer’s guide  Access pixel data  How to write a filter ITK Features Data representations Filtering Applications University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 37

38. Pixel access in ITK There are many ways to access pixels in ITK Direct pixel access Iterators - Index in data space - Physical position, in physical space University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 38

39. Direct pixel access in ITK  The simplest way is to directly address a pixel by knowing either its: The Index object is used to access pixels in an image, in data space ImageType::IndexType pixelIndex; pixelIndex[0] = 27; // x direction pixelIndex[1] = 29; // y direction // To set a pixel ImageType::PixelType pixelValue = 149; Image->SetPixel( pixelIndex, pixelValue ); // To get a pixel ImageType::PixelType pixelValue; pixelValue = Image->GetPixel( pixelIndex ); University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 39

40. Pixel access in ITK with iterators  An iterator is described as walking its iteration region.  There is no restriction on the dimensionality of the image or on the pixel type of the image. University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 40

41. Pixel access in ITK with iterators  Moving iterators  Accessing data - GoToBegin() - GoToEnd() - operator++() - operator--() - bool IsAtEnd() - bool IsAtBegin() - IndexType GetIndex() - … - PixelType Get() - void Set( PixelType ) University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 41

42. Pixel access in ITK with iterators  Iteration loops example (1D, 2D, 3D, 4D…)  Example on a volume (500x500x500)  Direct pixel access: 43.39 s.  Pixel access with iterators:4.48 s. typedef itk::ImageRegionIterator IteratorType; IteratorType it( image, image->GetRequestedRegion() ); for( it.GoToBegin(); !it.IsAtEnd(); it++ ) { std::cout << “Index : ” << it.GetIndex() << std::endl; std::cout << “Value : ” << it.Get() << std::endl; } University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 42

43. Pixel access in ITK with iterators  Classic image iterators  ImageRegionIterator (basic ITK image iterator)  ImageRegionIteratorWithIndex  Line-by-line iterators  ImageLinearIteratorWithIndex (NextLine(), …)  ImageSliceIteratorWithIndex (NextSlice(), …)  Random iterators  ImageRandomConstIteratorWithIndex  Neighborhood iterator  NeighborhoodIterator University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 43

44. Pixel access in ITK with iterators  Neighborhood iterator University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 44

45. Class hierarchy itk::DataObject itk::Object itk::ImageBase itk::Image itk::ProcessObject itk::ImageSource itk::ImageToImageFilter University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 45

46. Filter typical element Input Image Output Image Filter Parameters typedef itk::ImageToImageFilter FilterType; University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 46

47. Inplace filter element Input Image Output Image Filter Parameters typedef itk::InPlaceImageFilter FilterType; The output bulk data is the same block of memory as the input bulk data. University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 47

48. Create an Image Filter namespace itk { template class MyImageFilter : public ImageToImageFilter { public: typedef MyImageFilter Self; typedef ImageToImageFilter Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; itkNewMacro( Self ); itkTypeMacro( MyImageFilter, ImageToImageFilter ); University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 48

49. Create an Image Filter protected: MyImageFilter() {} virtual ~MyImageFilter() {} virtual void GenerateData() {};// The real work private: MyImageFilter( const Self & ); // purposely not implemented void operator=( const Self & ); // purposely not implemented }; // end of class } // end of namespace itk University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 49

50. Typical Declarations: Traits and Macros  Traits  Self  Superclass  Pointer  ConstPointer  Macros  NewMacro  TypeMacro University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 50

51. Create an Image Filter #include “MyImageFilter.h” int main() { typedef itk::Image InputImageType; typedef itk::Image OutputImageType; typedef itk::MyImageFilter< InputImageType, OutputImageType > FilterType; FilterType::Pointer filter = FilterType::New(); Filter->SetInput( reader->GetOuput() ); Filter->Update( ); OutputImageType::Pointer image = Filter->GetOutput( ); } University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 51

52. References  http://www.itk.org/ItkSoftwareGuide.pdf http://www.itk.org/ItkSoftwareGuide.pdf  http://www.itk.org/Doxygen/html/index.html http://www.itk.org/Doxygen/html/index.html  http://www.itk.org/HTML/Tutorials.htm http://www.itk.org/HTML/Tutorials.htm University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 52

53. References  http://caddlab.rad.unc.edu/Public/InsightIn90Minutes.ppt  ITK CVS Online documentation http://www.itk.org/Doxygen/html/index.html  ITK 2.0 (preliminary) Software Guide http://prdownloads.sourceforge.net/itk/ItkSoftwareGuide- 2.0.0.pdf?download  http://www.itk.org/CourseWare/Training/GettingStarted-I.pdf http://www.itk.org/CourseWare/Training/GettingStarted-I.pdf  Neurolib tutorial (excellent for research use of ITK) http://www.ia.unc.edu/dev/tutorials/InstallLib/index.htm University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 53

54. The End University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 54