ITK  4.8.0
Insight Segmentation and Registration Toolkit
* Copyright Insight Software Consortium
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* See the License for the specific language governing permissions and
* limitations under the License.
// Software Guide : BeginLatex
// This example shows how to instantiate an
// \subdoxygen{Statistics}{ImageToListSampleAdaptor} object and plug-in an
// \doxygen{Image} object as the data source for the adaptor.
// \index{itk::Statistics::Image\-To\-List\-Adaptor}
// \index{itk::Statistics::Scalar\-Image\-To\-List\-Adaptor|}
// \index{itk::Statistics::Joint\-Domain\-Image\-To\-List\-Adaptor}
// In this example, we use the ImageToListSampleAdaptor class that requires the
// input type of Image as the template argument. To users of the
// ImageToListSampleAdaptor, the pixels of the input image are treated as
// measurement vectors. The ImageToListSampleAdaptor is one of two adaptor classes
// among the subclasses of the \subdoxygen{Statistics}{Sample}. That means an
// ImageToListSampleAdaptor object does not store any real data. The data comes
// from other ITK data container classes. In this case, an instance of the
// Image class is the source of the data.
// To use an ImageToListSampleAdaptor object, include the header file for the
// class. Since we are using an adaptor, we also should include the header
// file for the Image class. For illustration, we use the
// \doxygen{RandomImageSource} that generates an image with random pixel
// values. So, we need to include the header file for this class. Another
// convenient filter is the \doxygen{ComposeImageFilter} which
// creates an image with pixels of array type from one or more input images
// composed of pixels of scalar type. Since an element of a
// Sample object is a measurement \emph{vector}, you
// cannot plug in an image of scalar pixels. However, if we
// want to use an image of scalar pixels without the help from the
// ComposeImageFilter, we can use the
// \subdoxygen{Statistics}{ScalarImageToListSampleAdaptor} class that is derived
// from the \subdoxygen{Statistics}{ImageToListSampleAdaptor}. The usage of the
// ScalarImageToListSampleAdaptor is identical to that of the ImageToListSampleAdaptor.
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
#include "itkImage.h"
// Software Guide : EndCodeSnippet
int main()
// Software Guide : BeginLatex
// We assume you already know how to create an image.
// The following code snippet will create a 2D image of float pixels
// filled with random values.
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef itk::Image<float,2> FloatImage2DType;
random->SetMin( 0.0 );
random->SetMax( 1000.0 );
typedef FloatImage2DType::SpacingValueType SpacingValueType;
typedef FloatImage2DType::PointValueType PointValueType;
SizeValueType size[2] = {20, 20};
random->SetSize( size );
SpacingValueType spacing[2] = {0.7, 2.1};
random->SetSpacing( spacing );
PointValueType origin[2] = {15, 400};
random->SetOrigin( origin );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
// We now have an instance of Image and need to cast it to an
// Image object with an array pixel type (anything derived from the
// \doxygen{FixedArray} class such as \doxygen{Vector},
// \doxygen{Point}, \doxygen{RGBPixel}, or
// \doxygen{CovariantVector}).
// Since the image pixel type is \code{float} in this example,
// we will use a single element \code{float} FixedArray as
// our measurement vector type. And that will also be our pixel type
// for the cast filter.
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef itk::FixedArray< float, 1 > MeasurementVectorType;
CasterType::Pointer caster = CasterType::New();
caster->SetInput( random->GetOutput() );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
// Up to now, we have spent most of our time creating an image
// suitable for the adaptor. Actually, the hard part of this example
// is done. Now, we just define an adaptor with the image type and
// instantiate an object.
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
ArrayImageType > SampleType;
SampleType::Pointer sample = SampleType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
// The final task is to plug in the image object to
// the adaptor. After that, we can use the common methods and
// iterator interfaces shown in Section~\ref{sec:SampleInterface}.
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
sample->SetImage( caster->GetOutput() );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
// If we are interested only in pixel values, the
// ScalarImageToListSampleAdaptor (scalar pixels) or the
// ImageToListSampleAdaptor (vector pixels) would be
// sufficient. However, if we want to perform some statistical
// analysis on spatial information (image index or pixel's physical
// location) and pixel values altogether, we want to have a
// measurement vector that consists of a pixel's value and physical
// position. In that case, we can use the
// \subdoxygen{Statistics}{JointDomainImageToListSampleAdaptor}
// class. With this class, when we call the
// \code{GetMeasurementVector()} method, the returned measurement
// vector is composed of the physical coordinates and pixel
// values. The usage is almost the same as with
// ImageToListSampleAdaptor. One important difference between
// JointDomainImageToListSampleAdaptor and the other two image
// adaptors is that the JointDomainImageToListSampleAdaptor has the
// \code{SetNormalizationFactors()} method. Each component of a
// measurement vector from the JointDomainImageToListSampleAdaptor
// is divided by the corresponding component value from the supplied
// normalization factors.
// Software Guide : EndLatex
return 0;