Medical Imaging
ITK
Dr. Engr. Sami ur Rahman
Assistant Professor
Department of Computer Science
University of Malakand
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
Installing ITK
 Download InsightTookit-2.0.1.zip from
http://www.itk.org/HTML/Download.htm
 Download CMake installer from
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
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
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 <infile> <outfile> <scale>
University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 5
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
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
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
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
File
Image
Gaussian
Image
Writer
File
University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 9
How to find what you need ?
 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
University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 10
How to integrate ITK
C++ Glue code
ITK
GUI
Image
Processing
MFC, QT,
BBTK, wxWin
Visualization
OpenGL, VTK
University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 11
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
Concepts (I)
Pipeline architecture
 Data pipeline
Image
Filter
Image
Filter
Image
Filter
Image
Image
University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 13
Concepts (I)
Pipeline architecture
 Pipeline architecture
1.Build Pipeline
2.Execute pipeline
Reader
File
Image
Gaussian
Image
Writer
File
University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 14
Concepts (II)
An aside: Smart pointer
 In C++ you typically allocate memory with new and desallocate it with
delete
Cat* pCat = new Cat;
pCat->Meaow();
delete pCat;
memory leak
 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
University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 15
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< T >
.02
…
1 2.5
…
0 .23
…
5 .47
…
4 5.6
…
3
std::vector< int >
std::vector< double >
std::vector< char* >
std::vector< point >
std::vector< image >
University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 16
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
Contents
 What is ITK
 Background
 Concepts
Data representations
Filtering
 Data representations
 Images and regions
 Pixel access in ITK
ITK Features
 Developer’s guide
 Access pixel data
 How to write a filter
Applications
University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 18
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
Class hierarchy
itk::Object
itk::DataObject
itk::ImageBase
itk::PointSet
itk::Image
itk::Mesh
University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 20
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
Images and regions
Algorithms only process a region of
an image that sits inside the current
buffer
LargestPossibleRegion::Size
BufferedRegion::Size
 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
RequestedRegion::Size
RequestedRegion::Index
BufferedRegion::Index
LargestPossibleRegion::Index
University Of Malakand | Department of Computer Science | Visual Computing
Images are templated
 Image ITK
itk::Image< PixelType, Dimension >
Pixel type
Dimensionality (value)
 Example
itk::Image< int, 2 >
itk::Image< char, 4 >
itk::Image< RGB, 3 >
itk::Image< std::vector< double >, 2 >
Unsigned char, 2
University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 23
Images and regions
 The favorite keyword
typedef
 Declaring an image type
typedef itk::Image< char, 2 > ImageType;
 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
University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 24
Images and regions
 Creating an image pointer
typedef itk::Image< char, 2 > ImageType;
ImageType::Pointer image = ImageType::New();
Pointer is typedef in itk::Image
Macro “big New”
 An image is created by invoking the New() operator from the
corresponding image type and assigning the result to a SmartPointer.
University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 25
Images and regions
 Creating an image
typedef itk::Image< char, 2 > 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
 Classes can have typedefs as members. In this case, SizeType is a
public member of itk::Image.
University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 26
Images and regions
 Creating the region of an image
typedef itk::Image< char, 2 > 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
Images and regions
 Allocate
typedef itk::Image< char, 2 > 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 );
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
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
Data space vs. “physical” space
 Data space is an N-d array with
integer indices, indexed from 0 to (Li
- 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
Data space vs. “physical” space
 Spacing: We can specify spacing by calling the SetSpacing() function in
Image.
ImageType::SpacingType spacing;
spacing[0] = 0.83; // x direction
spacing[1] = 2.15; // y direction
Image->SetSpacing( spacing );
 Origin: Similarly, we can set the image origin
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
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< char, 2 > 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
ITK Image IO
Image
File
PNGImageIO
GIPLImageIO
CustomImageIO
ImageFileReader
MetaImageIO
Image
AnalyzeImageIO
VTKImageIO
DICOMImageIO
Loadable Factories
University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 33
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
ITK Image IO
#include “itkImageFileReader.h”
#include “itkImageFileWriter.h”
int main()
{
typedef itk::Image< unsigned char, 2 > ImageType;
typedef itk::ImageFileReader< ImageType > ReaderType;
ReaderType::Pointer reader = ReaderType::New();
reader->SetFilename( inputFilename );
reader->Update( ); // reader->Update( );
typedef itk::ImageFileWiter< ImageType > 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
}
Image typedef
 Classes can have typedefs as members.
typedef itk::Image< char, 2 > 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
Contents
 What is ITK
 Background
 Concepts
Data representations
Filtering
 Data representations
 Images and regions
 Pixel access in ITK
ITK Features
 Developer’s guide
 Access pixel data
 How to write a filter
Applications
University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 37
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
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
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
Pixel access in ITK with iterators
 Moving iterators
- GoToBegin()
- GoToEnd()
- operator++()
- operator--()
- bool IsAtEnd()
- bool IsAtBegin()
- IndexType GetIndex()
- …
 Accessing data
- PixelType Get()
- void Set( PixelType )
University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 41
Pixel access in ITK with iterators
 Iteration loops example (1D, 2D, 3D, 4D…)
typedef itk::ImageRegionIterator< ImageType > 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;
}
 Example on a volume (500x500x500)
 Direct pixel access:
 Pixel access with iterators:
43.39 s.
4.48 s.
University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 42
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
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
Class hierarchy
itk::Object
itk::DataObject
itk::ProcessObject
itk::ImageBase
itk::ImageSource
itk::Image
itk::ImageToImageFilter
University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 45
Filter typical element
typedef itk::ImageToImageFilter
< ImageType, ImageType > FilterType;
Filter
Input
Image
Output
Image
Parameters
University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 46
Inplace filter element
typedef itk::InPlaceImageFilter
< ImageType, ImageType > FilterType;
Filter
Input
Image
Output
Image
Parameters
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
Create an Image Filter
namespace itk {
template< class TInput, class TOutput>
class MyImageFilter :
public ImageToImageFilter< class TInput, class TOutput>
{
public:
typedef MyImageFilter
Self;
typedef ImageToImageFilter< TInput, TOutput > Superclass;
typedef SmartPointer< Self >
Pointer;
typedef SmartPointer< const Self >
ConstPointer;
itkNewMacro( Self );
itkTypeMacro( MyImageFilter, ImageToImageFilter );
University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 48
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
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
Create an Image Filter
#include “MyImageFilter.h”
int main()
{
typedef itk::Image< unsigned char, 2 > InputImageType;
typedef itk::Image< unsigned short, 2 > 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
}
References
 http://www.itk.org/ItkSoftwareGuide.pdf
 http://www.itk.org/Doxygen/html/index.html
 http://www.itk.org/HTML/Tutorials.htm
University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 52
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/ItkSoftwareGuide2.0.0.pdf?download
 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
The End
University Of Malakand | Department of Computer Science | Visual Computing Research Group | Dr. Engr.Sami ur Rahman | 54