SimpleITK Tutorial

Transcription

SimpleITK Tutorial

SimpleITK Tutorial
Image processing for mere mortals
Insight Software Consortium
Sept 23, 2011
January 10, 2012
(Insight Software Consortium)
SimpleITK - MICCAI 2011
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This presentation is copyrighted by
The Insight Software Consortium
distributed under the
Creative Commons by Attribution License 3.0
http://creativecommons.org/licenses/by/3.0
What this Tutorial is about
Provide working knowledge of the SimpleITK platform
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Program
Virtual Machines Preparation (10min)
Introduction (15min)
Basic Tutorials I (45min)
Short Break (10min)
Basic Tutorials II (45min)
Co↵ee Break (30min)
Intermediate Tutorials (45min)
Short Break (10min)
Advanced Topics (40min)
Wrap-up (10min)
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Tutorial Goals
Gentle introduction to ITK
Introduce SimpleITK
Provide hands-on experience
Problem solving, not direction following
...but please follow directions!
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ITK Overview
How many are familiar with ITK?
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Ever seen code like this?
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// Setup image types .
typedef
float InputPi xelType ;
typedef
float O ut pu t Pi xe lT y pe ;
typedef itk : : Image<InputPixelType , 2> Inpu tImageTy pe ;
typedef itk : : Image<OutputPixelType ,2> O ut pu tI m ag eT yp e ;
// Filter type
typedef itk : : DiscreteGaussianImageFilter<
InputImageType , O ut pu tI m ag eT yp e >
FilterType ;
// Create a filter
FilterType : : Pointer filter = FilterType : : New ( ) ;
// Create the p i p e l i n e
filter >SetInput ( reader >GetOutput ( ) ) ;
filter >SetVariance ( 1 . 0 ) ;
filter >S e t M a x i m u m K e r n e l W i d t h ( 5 ) ;
filter >Update ( ) ;
Ou t pu tI ma g eT yp e : : Pointer blurred = filter >GetOutput ( ) ;
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What if you could write this?
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import SimpleITK
input = SimpleITK . ReadImage ( filename )
output = SimpleITK . D i s c r e t e G a u s s i a n F i l t e r ( input , 1 . 0 , 5 )
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What if you could write this?
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import SimpleITK
input = SimpleITK . ReadImage ( filename )
output = SimpleITK . D i s c r e t e G a u s s i a n F i l t e r ( input , 1 . 0 , 5 )
We are here to tell you that you can...
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Goals of SimpleITK
Be an “on-ramp” for ITK
Simplify the use of ITK by
Providing a templateless, typeless layer for ITK in C++
Providing wrappings in scripting languages
Providing access to most ITK algorithms
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SimpleITK Architectural Overview
Conceptually, SimpleITK is an application library built on ITK
All functionality provided by ITK
Components:
Template expansion system
C++ library
Small SWIG definition (more details later)
“Glue” code for several scripting languages
Some language utilities
Open Source, Apache licensed project
(http://www.opensource.org/licenses/apache2.0.php)
Hosted by GitHub (https://github.com/SimpleITK/SimpleITK)
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Templates in ITK
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typedef unsigned char PixelType ;
enum {Imag eDimensi on = 2 } ;
typedef itk : : Image<PixelType , ImageDimension> ImageType ;
typedef itk : : Vector<float , ImageDimension> VectorType ;
typedef itk : : Image<VectorType , ImageDimension> FieldType ;
typedef itk : : Image<VectorType : : ValueType , ImageDimension> Float ImageTyp e ;
typedef ImageType : : IndexType IndexType ;
typedef ImageType : : SizeType
SizeType ;
typedef ImageType : : RegionType RegionType ;
typedef itk : : M u l t i R e s o l u t i o n P D E D e f o r m a b l e R e g i s t r a t i o n
<ImageType , ImageType , FieldType> R e g i s t r a t i o n T y p e ;
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Template Freedom
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using itk : : simple ;
// Read the image file
I ma ge Fi l eR ea de r reader ;
reader . SetFileName ( " / my / fancy / file . nrrd " ) ;
Image image = reader . Execute ( ) ;
// This filters perform a gaussian bluring with sigma in
// ph y s i c a l space . The output image will be of real type .
S m o o t h i n g R e c u r s i v e G a u s s i a n I m a g e F i l t e r gaussian ;
gaussian . SetSigma ( 2 . 0 ) ;
Image blurredImage = gaussian . Execute ( image ) ;
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Programming Models
Object oriented
Function oriented
More about this later
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Wrapping
Transformed by SWIG
Parses header and “interface” files
Automatically creates scripting “glue”
Wrappings available for:
Python
Java
C#
Tcl, Lua, Ruby
Others as requested. . .
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Image Anatomy
SimpleITK images contain itk::Image
Hides the templates
Adds useful “utility” functions
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Filter Anatomy
SimpleITK filters contain itk::ImageToImageFilter
Hides the templates
Adds useful “utility” functions
(Mainly) output = Filter.Execute ( input )
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Basic Tutorial
Basic Tutorial
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Note on the Tutorial
Most examples will be Python
Obvious translation to other languages
C++ usage (generally) obvious
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Image Class
Creation
Number of dimensions, size, origin, spacing
Pixel access
Back to iPython (Examples/BasicTutorial1/Image.py)
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What just happened?
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import SimpleITK as sitk
# Create an image
image = sitk . Image ( 2 5 6 , 2 5 6 , 2 5 6 , sitk . sitkInt16 ) ;
# How about 2 d ?
twoD = sitk . Image ( 6 4 , 6 4 , sitk . sitkFloat32 )
sitk is the module
Image is the constructor for the Image class
Height, width, depth (omit depth for 2D images)
Datatype (more on this later)
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What just happened?
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# Addressing pixels
image . GetPixel ( 0 , 0 , 0 )
image . SetPixel ( 0 , 0 , 0 , 1 )
image . GetPixel ( 0 , 0 , 0 )
Get the voxel value at [0,0,0]?
Hmm, I don’t like it, so set to 1
What is the value at [0,0,0] now?
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What just happened?
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# Addressing pixels
image [ 0 , 0 , 0 ]
image [ 0 , 0 , 0 ] = 10
image [ 0 , 0 , 0 ]
Without warning, we sprinkled syntatic sugar on you!
image[0,0,0] is shorthand for Image.GetPixel(0,0,0)
image[0,0,0] = 10 is shorthand for Image.SetPixel(0,0,0,10)
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Summary
Images are created using SimpleITK.Image ( w, h, d, Type )
Images can be 2- or 3-dimensional
Images can describe themselves
Images have simple pixel accessors
Questions before we move on?
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Memory Management
Memory Management
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Images
Images...
usually allocated on the stack
are copy-on-write
use internal smart-pointers
Back to iPython (Examples/BasicTutorial1/MemoryManagement.py)
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Image Memory Management
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image = SimpleITK . Image ( 3 2 , 3 2 , 3 2 , SimpleITK . sitkInt16 )
print image
...
Image ( 0 x94f2d98 )
Reference Count : 1
...
# Clone image
b = SimpleITK . Image ( image )
print image
...
Reference Count : 2
...
print b
...
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Image Memory Management
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print b
...
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b [0 ,0 ,0] = 1
print b
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Image ( 0 x94f4cb0 )
Reference Count : 1
...
print image
...
Reference Count : 1
...
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Filters
Filters...
usually allocated on the stack
tend to clean up after themselves
do not hold on to images
no access to the ITK pipeline
...more on this later...
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Memory Management Strategies
C++. . .
No need for explicit management
Let images clean up after themselves
Let filters clean up after themselves
Wrapped. . .
Utilize language-specific memory management
Automatic in Python, Java, Ruby, C#, Lua
More manual in Tcl
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Basic Tutorial 2
Basic Tutorial 2
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Hands On
Hands On
Examples/BasicTutorial2/Filters.py
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What just happened?
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# Simple smoothing
smooth = sitk . S m o o t h i n g R e c u r s i v e G a u s s i a n ( image , 2 . 0 )
sitk . Show ( sitk . Subtract ( image , smooth ) )
...
RuntimeError : Exception thrown in SimpleITK Subtract : . . .
sitk : : ERROR : Both images for S u b t r a c t I m a g e F i l t e r don ’t match type or dimension !
...
The output of SmoothingRecursiveGaussian is of type float
The input image is signed short
Most SimpleITK filters with 2 inputs require the same type
Let’s fix the problem
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Introducing Cast
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# Much better
print " Before : " , smooth . G e t P i x e l I D T y p e A s S t r i n g ( )
smooth = sitk . Cast ( smooth , image . G et Pi xe l ID Va lu e ( ) )
print " After : " , smooth . G e t P i x e l I D T y p e A s S t r i n g ( )
sitk . Show ( sitk . Subtract ( image , smooth ) , " D i f f W i t h G a u s s i a n " )
Back to iPython (Examples/BasicTutorial2/Filters.py)
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Sizes and Indices
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# Extract
size = [ 6 4 , 6 4 , 1 ]
start = [ 6 4 , 0 , 0 ]
sitk . Show ( sitk . Extract ( image , size , start ) , " Extracted " )
in C++ / ITK code this would use
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typedef unsigned char PixelType ;
enum {Imag eDimensi on = 2 } ;
typedef itk : : Image<PixelType , ImageDimension> ImageType ;
typedef ImageType : : IndexType IndexType ;
typedef ImageType : : SizeType
SizeType ;
typedef ImageType : : RegionType RegionType ;
SimpleITK uses STL vectors
Wrapping converts to language-specific constructs (tuples/arrays)
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Pixel-wise Operators
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# Use pixel - wise operators
sitk . Show ( 127 ⇤ image + 127 ⇤ sitk . BinaryErode ( image ) , " ThinErosion " )
Table: SimpleITK Pixel-wise Operators
Operator
+
†
††
⇤
/
&
|
⇠
⇤⇤††
Equivalant
Add, AddConstantTo
Subtract, SubtractConstantFrom
Multiply, MultiplyByConstant
Divide, DivideByConstant
And “and”
Or “or”
Not “not”
Pow, PowToConstant
Usage†
A + B, s + A, s + B
A B, s A, s B
A ⇤ B, s ⇤ A, s ⇤ B
A/B, s/A, B/s
A&B
A|B
A
A ⇤ ⇤B, A ⇤ ⇤s
A and B are images (2D or 3D), s is a scalar
python only
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Operator Example
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sitk . Hash ( image + 2 )
sitk . Hash ( sitk . AddConstantTo ( image , 2 ) )
sitk . Hash ( image ⇤ 2 )
sitk . Hash ( sitk . M u l t i p l y B y C o n s t a n t ( image , 2 ) )
Hash calculates the MD5 or SHA1 hash of the image data
Used to tell if two images are exactly identical
Back to iPython (Examples/BasicTutorial2/Morphology.py)
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SimpleITK with Numpy
Numpy is:
Python’s universal numerics foundation
Provides high performance basic linear algebra
numpy interface functions:
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def G e t A r r a y F r o m I m a g e ( image ) :
""" Get a numpy array from a SimpleITK Image . """
def G e t I m a g e F r o m A r r a y ( arr ) :
""" Get a SimpleITK Image from a numpy array . """
Both of these methods do a deep copy of the image
Ensures safety, bypassing error-prone memory issues
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Python Example
To iPython (Examples/InteractiveTutorial/05-02-Numpy.py)
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Image Measurements
Image Measurements
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The Scenerio
You collected 1000 Di↵usion Weighted Imaging scan sessions
Now, compute the fractional anisotropy measures for a set of
anatomical regions
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Python Example
To iPython (Examples/InteractiveTutorial/MeasureRegions.py)
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Feature Detection
Feature Detection
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Edge Detection
CannyEdgeDetection
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Image C a n n y E d g e D e t e c t i o n ( c o n s t Image& ,
double inLowerThreshold = 0.0 ,
double inUpperThreshold = 0.0 ,
s t d : : v e c t o r <d o u b l e> i n V a r i a n c e = s t d : : v e c t o r <d o u b l e >(3 , 0 . 0 ) ,
s t d : : v e c t o r <d o u b l e> inMaximumError = s t d : : v e c t o r <d o u b l e >(3 , 0 . 0 1 ) ) ;
SobelEdgeDetection
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Image S o b e l E d g e D e t e c t i o n ( c o n s t Image&
);
ZeroCrossingBasedEdge
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Image Z e r o C r o s s i n g B a s e d E d g e D e t e c t i o n ( c o n s t Image& ,
double i n V a r i a n c e = 1 ,
u i n t 8 t i n F o r e g r o u n d V a l u e = 1u ,
u i n t 8 t i n B a c k g r o u n d V a l u e = 0u ,
d o u b l e inMaximumError = 0 . 1 ) ;
GradientMagnitudeRecursiveGaussian
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Image G r a d i e n t M a g n i t u d e R e c u r s i v e G a u s s i a n ( c o n s t Image& ,
double inSigma = 1.0 ,
bool inNormalizeAcrossScale = f a l s e ) ;
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Image Derivatives
Derivative
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Image D e r i v a t i v e ( c o n s t Image& ,
u n s i g n e d i n t i n D i r e c t i o n = 0u ,
u n s i g n e d i n t i n O r d e r = 1u ,
bool inUseImageSpacing = true ) ;
RecursiveGaussian
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Image R e c u r s i v e G a u s s i a n ( c o n s t Image& ,
bool inNormalizeAcrossScale = fal se ,
i t k : : s i m p l e : : R e c u r s i v e G a u s s i a n I m a g e F i l t e r : : OrderEnumType i n O r d e r = i t k : : s i m p l
u n s i g n e d i n t i n D i r e c t i o n = 0u ) ;
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Advanced Topics
Advanced Topics
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Building SimpleITK in 5 Easy Commands
git clone --recursive https://github.com/SimpleITK/SimpleITK
mkdir SimpleITK/SimpleITK-build
cd SimpleITK/SimpleITK-build
cmake ../SuperBuild
make -j 5
Or as part of Slicer
ITK_VERSION_MAJOR=4
Slicer_USE_SimpleITK=ON
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More complete version
Check out the code from GitHub
(https://github.com/SimpleITK/SimpleITK)
Run CMake (http://www.cmake.org/) using
SimpleITK/SuperBuild as the source directory
Build using your favorite compiler
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Supported Platforms
Windows: Visual Studio 10
Windows: Visual Studio 9 (Requires TR1 service pack)
Mac OSX: gcc 4.x (Xcode 4.x)
Linux: gcc 4.x
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SimpleITK Architecture
SimpleITK Architecture
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Filter Anatomy
SimpleITK filters create ITK filters
Templated based on input type
Output type is usually the same as input type
Instantiated for many possible image types
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Image and Filter Types
Dimensions
Vector Types
2 dimensional
3 dimensional
int8 t
uint8 t
int16 t
uint16 t
int32 t
uint32 t
float
double
Scalar types
int8 t
uint8 t
int16 t
uint16 t
int32 t
uint32 t
float
double
std :: complex < float >
std :: complex < double >
Label Types
uint8 t
uint16 t
uint32 t
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Filter Anatomy
Filter interrogates input
Instantiates proper ITK filter
Executes ITK filter
Constructs output from ITK image
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Using Filters
Using Filters
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Object Paradigm (C++)
1 #i n c l u d e <S i m p l e I T K . h>
2 namespace sitk = itk : : simple ;
3 ...
4 // Create a s m o o t h i n g filter
5 sitk : : S m o o t h i n g R e c u r s i v e G a u s s i a n I m a g e F i l t e r gaussian ;
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7 // Set a p a r a m e t e r
8 gaussian . SetSigma ( 2 . 0 ) ;
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10 // " Execute " the Filter
11 sitk : : Image blurredImage = gaussian . Execute ( image ) ;
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Flexibility
3 ...
4 // Create a s m o o t h i n g filter
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7 // Set p a r a m e t e r ( s ) , then execute
8 sitk : : Image blurredImage = gaussian
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. SetSigma ( 2 . 0 )
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. Execute ( image ) ;
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3 ...
4 blurredImage = sitk : : S m o o t h i n g R e c u r s i v e G a u s s i a n I m a g e F i l t e r ( )
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. SetSigma ( 2 . 0 )
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. SetRadius ( 5 )
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. Execute ( image ) ;
One line: create anonymous filter, set parameters, and execute
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“Function” Paradigm (C++)
3 ...
4 // Call the f u n c t i o n version
5 // NB : Drop the " I m a g e F i l t e r "!
6 // S i g n a t u r e :
7 /*
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sitk :: Image S m o o t h i n g R e c u r s i v e G a u s s i a n (
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const Image & ,
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bool i n N o r m a l i z e A c r o s s S c a l e = false );
12 */
13 sitk : : Image blurredImage = sitk : : S m o o t h i n g R e c u r s i v e G a u s s i a n (
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image ,
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2.0 ,
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false ) ;
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Mix & Match (C++)
3 ...
4 // Get our g a u s s i a n ready
6 gaussian . SetSigma ( 2 . 0 ) ;
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8 // What is the effect on the image
9 sitk : : Image difference = sitk : : Subtract (
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image ,
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gaussian . Execute ( image )
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);
13 sitk : : Image difference2 = sitk : : Subtract (
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image ,
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sitk : : S m o o t h i n g R e c u r s i v e G a u s s i a n (
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image , 2 . 0
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)
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);
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Code Philosophy
Code Philosophy
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Filter Class Overview (C++)
1 class S m o o t h i n g R e c u r s i v e G a u s s i a n I m a g e F i l t e r :
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public ImageFilter {
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typedef S m o o t h i n g R e c u r s i v e G a u s s i a n I m a g e F i l t e r Self ;
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/* * Default C o n s t r u c t o r that takes no a r g u m e n t s
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and i n i t i a l i z e s default p a r a m e t e r s */
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SmoothingRecursiveGaussianImageFilter ( ) ;
In line 1, we declare a subclass of ImageFilter
Line 3 creates a special typedef for use later
The default constructor is line 7 (never any parameters)
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Filter Class Overview (C++) Continued
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2
/* * Define the pixels types s u p p o r t e d by this filter */
typedef B a s i c P i x e l I D T y p e L i s t P ix e lI DT yp e Li st ;
Notice PixelIDTypeList in line 2
Used to instantiate ITK filters
Determines valid input image types
BasicPixelIDTypeList expands to:
int8 t, uint8 t
int16 t, uint16 t
int32 t, uint32 t
float, double
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Self& SetSigma ( double t ) { . . . return ⇤this ; }
double GetSigma ( ) { return this >m_Sigma ; }
Self& S e t N o r m a l i z e A c r o s s S c a l e ( bool t ) { . . . }
Self& N o r m a l i z e A c r o s s S c a l e O n ( ) { . . . }
Self& N o r m a l i z e A c r o s s S c a l e O f f ( ) { . . . }
bool G e t N o r m a l i z e A c r o s s S c a l e ( ) { . . . }
Get/Set parameters
Set methods always return Self & (more later)
Generally, a direct mapping to ITK
Boolean parameters generate On and O↵ methods
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/* * Name of this class */
std : : string GetName ( ) const { . . . }
/* * Print o u r s e l v e s out */
std : : string ToString ( ) const ;
Return the name and description of the filter
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1
/* * Execute the filter on the input image */
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Image Execute ( const Image & ) ;
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/* * Execute the filter with p a r a m e t e r s */
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Image Execute ( const Image &,
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double inSigma ,
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bool i n N o r m a l i z e A c r o s s S c a l e ) ;
8 } ; /* End of class S m o o t h i n g R e c u r s i v e G a u s s i a n */
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10 Image S m o o t h i n g R e c u r s i v e G a u s s i a n ( const Image& ,
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double inSigma = 1 . 0 ,
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bool i n N o r m a l i z e A c r o s s S c a l e = false ) ;
Run the filter on an image and return the result
Notice extra function (line 10), adds flexibility
Drop ImageFilter from class name to get function name
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Questions?
Questions?
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Using ITK with SimpleITK
Problem: Use ITK from SimpleITK (or vice versa)
./ToITK input.nii output.nii
Steps:
Load image using SimpleITK
Filter using ITK
Save using OpenCV
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To ITK
Starting code: ToITK/ToITK.cxx
Directory:
SimpleITK-MICCAI-2011-Tutorial/Examples/AdvancedTutorial
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namespace s i t k = i t k : : s i m p l e ;
...
// Load t h e image v i a S i m p l e I T K
s i t k : : Image s i t k I m a g e = s i t k : : ReadImage ( i n p u t F i l e n a m e ) ;
// C o n s t r u c t t h e ITK P i p e l i n e
// L i n k p i p e l i n e t o S i m p l e I T K
// Update p i p e l i n e
// C r e a t e o u t p u t S i m p l e I T K image
// Save image v i a S i m p l e I T K
s i t k : : W r i t e I m a g e ( sOutput , o u t p u t F i l e n a m e ) ;
r e t u r n EXIT SUCCESS ;
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ToITK – Step 1: Construct the ITK Pipeline
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// C o n s t r u c t t h e ITK P i p e l i n e
t y p e d e f i t k : : Image<f l o a t ,3> ImageType ;
t y p e d e f i t k : : M i r r o r P a d I m a g e F i l t e r <ImageType , ImageType> P a d F i l t e r T y p e ;
P a d F i l t e r T y p e : : S i z e T y p e upperBound , l o w e r B o u n d ;
P a d F i l t e r T y p e : : P o i n t e r pad = P a d F i l t e r T y p e : : New ( ) ;
f o r ( u n s i g n e d i n t i = 0 ; i < 3 ; i++ )
{
upperBound [ i ] = s i t k I m a g e . G e t S i z e ( ) [ i ] ;
lowerBound [ i ] = s i t k I m a g e . G e t S i z e ( ) [ i ] ;
}
pad >SetPadUpperBound ( upperBound ) ;
pad >SetPadLowerBound ( l o w e r B o u n d ) ;
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ToITK – Step 2: Link pipeline to SimpleITK
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// L i n k p i p e l i n e t o S i m p l e I T K
ImageType : : P o i n t e r i n p u t I m a g e = ( ImageType ⇤) s i t k I m a g e . G e t I m a g e B a s e ( ) ;
pad >S e t I n p u t ( i n p u t I m a g e ) ;
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ToITK – Step 3: Update ITK Pipeline
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// Update p i p e l i n e
pad >Update ( ) ;
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ToITK – Step 4: Create the SimpleITK output image
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// C r e a t e o u t p u t S i m p l e I T K image
s i t k : : Image s O u t p u t ( pad >GetOutput ( ) ) ;
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ToITK – (Optional) Step 5: Show
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// ( O p t i o n a l ) Show t h e r e s u l t s
s i t k : : Show ( s O u t p u t ) ;
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To ITK Solution
~/Source/AdvancedTutorial-build/ToITK/ToITKSolution \
~/Source/SimpleITK/Testing/Data/Input/RA-Float.nrrd \
/tmp/foo.nii
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Quick Examples from Other Languages
Simple Gaussian in 8
languages
AdvancedTutorial/SimpleGaussian/SimpleGaussian.*
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C++
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# include < SimpleITK .h >
namespace sitk = itk :: simple ;
int main ( int argc , char * argv [] ) {
// Read the image file
sitk :: I ma g eF il eR e ad er reader ;
reader . SetFileName ( std :: string ( argv [1] ) );
sitk :: Image image = reader . Execute ();
// This filters perform a gauss ian bluring with sigma in physical
// space . The output image will be of real type .
sitk :: S m o o t h i n g R e c u r s i v e G a u s s i a n I m a g e F i l t e r gaussian ;
gaussian . SetSigma ( atof ( argv [2] ) );
sitk :: Image blurredImage = gaussian . Execute ( image );
// Covert the real output image back to the original pixel type , to
// make writing easier , as many file formats don ’t support real
// pixels .
sitk :: C as tI m ag eF il t er caster ;
caster . S e t O u t p u t P i x e l T y p e ( image . G et Pi x el ID Va l ue () );
sitk :: Image outputImage = caster . Execute ( blurredImage );
// write the image
sitk :: I ma ge F il eW ri t er writer ;
writer . SetFileName ( std :: string ( argv [3] ) );
writer . Execute ( outputImage );
return 0;
}
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Ruby
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require ’ simpleitk ’
if ARGV . length != 3 then
puts " Usage : Simpl eGaussia n < input > < sigma > < output > " ;
exit ( 1 )
end
reader = Simpleitk : : I ma ge Fi l eR ea de r . new
reader . set_file_name ( ARGV [ 0 ] )
image = reader . execute
in putPixe lType = image . g e t _ p i x e l _ i d v a l u e
gaussian = Simpleitk : : S m o o t h i n g R e c u r s i v e G a u s s i a n I m a g e F i l t e r . new
gaussian . set_sigma ARGV [ 1 ] . to_f
image = gaussian . execute image ;
caster = Simpleitk : : C as tI ma g eF il te r . new
caster . s e t _ o u t p u t _ p i x e l _ t y p e inputP ixelType
image = caster . execute image
writer = Simpleitk : : I ma ge Fi l eW ri te r . new
writer . set_file_name ARGV [ 2 ]
writer . execute image
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R
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# Run with :
#
# Rscript -- vanilla S i m p l e G a u s s i a n . R
#
input sigma output
library ( SimpleITK )
args <- commandArgs ( TRUE )
myreader <- I ma ge Fi l eR ea de r ( )
myreader <- I ma ge Fi l eR ea de r _ SetFileName ( myreader , args [ [ 1 ] ] )
myimage <- Im ag e Fi le Re a de r _ Execute ( myreader )
pixeltype <- Image _ G e tP ix el I DV al ue ( myimage )
myfilter <- S m o o t h i n g R e c u r s i v e G a u s s i a n I m a g e F i l t e r ( )
myfilter <- S m o o t h i n g R e c u r s i v e G a u s s i a n I m a g e F i l t e r _ SetSigma ( myfilter , as . real ( args [ 2 ] ) )
smoothedimage <- S m o o t h i n g R e c u r s i v e G a u s s i a n I m a g e F i l t e r _ Execute ( myfilter , myimage )
mycaster <- C as tI ma g eF il te r ( )
mycaster <- C as tI ma g eF il te r _ S e t O u t p u t P i x e l T y p e ( mycaster , pixeltype )
castedimage <- Ca st Im a ge Fi lt e r _ Execute ( mycaster , soothedimage )
mywriter <- I ma ge Fi l eW ri te r ( )
mywriter <- I ma ge Fi l eW ri te r _ SetFileName ( mywriter , args [ [ 3 ] ] )
mywriter <- I ma ge Fi l eW ri te r _ Execute ( mywriter , castedimage )
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C#
1 u s i n g System ;
2 using itk . simple ;
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4 namespace itk . simple . examples {
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class Sim pleGauss ian {
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static void Main ( string [ ] args ) {
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try {
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if ( args . Length < 3 ) {
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Console . WriteLine ( " Usage : Simp leGauss ian < input > < sigma > < output > " ) ;
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return ;
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}
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// Read input image
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I ma ge Fi l eR ea de r reader = new I ma ge F il eR ea d er ( ) ;
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reader . SetFileName ( args [ 0 ] ) ;
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Image image = reader . Execute ( ) ;
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// Execute Gaussian s m o o t h i n g filter
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S m o o t h i n g R e c u r s i v e G a u s s i a n I m a g e F i l t e r gaussian = new S m o o t h i n g R e c u r s i v e G a
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gaussian . SetSigma ( Double . Parse ( args [ 1 ] ) ) ;
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image = gaussian . Execute ( image ) ;
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// Write output image
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I ma ge Fi l eW ri te r writer = new I ma ge F il eW ri t er ( ) ;
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writer . SetFileName ( args [ 2 ] ) ;
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writer . Execute ( image ) ;
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} catch ( Exception ex ) {
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Console . WriteLine ( ex ) ;
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}
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}
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}
32 }
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Java
1 import org . itk . simple . ⇤ ;
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3 class Simp leGaussi an {
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public static void main ( String argv [ ] ) {
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if ( argv . length < 3 ) {
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System . out . println ( " Usage : java S impleGau ssian < input > < sigma > < output > " ) ;
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return ;
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}
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org . itk . simple . I ma ge Fi l eR ea de r reader = new org . itk . simple . I ma ge Fi l eR ea de r ( ) ;
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reader . setFileName ( argv [ 0 ] ) ;
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Image img = reader . execute ( ) ;
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S m o o t h i n g R e c u r s i v e G a u s s i a n I m a g e F i l t e r filter =
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new S m o o t h i n g R e c u r s i v e G a u s s i a n I m a g e F i l t e r ( ) ;
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filter . setSigma ( Double . valueOf ( argv [ 1 ] ) . doubleValue ( ) ) ;
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Image blurredImg = filter . execute ( img ) ;
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Ca s tI ma ge F il te r caster = new C as tI ma g eF il te r ( ) ;
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caster . s e t O u t p u t P i x e l T y p e ( img . g et Pi xe l ID Va lu e ( ) ) ;
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Image castImg = caster . execute ( blurredImg ) ;
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Im a ge Fi le W ri te r writer = new I ma ge Fi l eW ri te r ( ) ;
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writer . setFileName ( argv [ 2 ] ) ;
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writer . execute ( castImg ) ;
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}
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31 }
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Lua
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if #arg < 3 then
print ( ” Usage : Simpl eGaussia n <input> <sigma> <output>” )
os . exit ( 1 )
end
reader = SimpleITK . I ma ge Fi l eR ea de r ( )
-- Re m e m b e r that Lua arrays are 1 - based ,
-- and that arg does not contain the a p p l i c a t i o n name !
reader : SetFileName ( arg [ 1 ] )
image = reader : Execute ( ) ;
in putPixe lType = image : G et Pi xe l ID Va lu e ( )
gaussian = SimpleITK . S m o o t h i n g R e c u r s i v e G a u s s i a n I m a g e F i l t e r ( )
gaussian : SetSigma ( arg [ 2 ] )
image = gaussian : Execute ( image ) ;
caster = SimpleITK . C as tI ma g eF il te r ( ) ;
caster : S e t O u t p u t P i x e l T y p e ( input PixelTy pe ) ;
image = caster : Execute ( image )
writer = SimpleITK . I ma ge Fi l eW ri te r ( )
writer : SetFileName ( arg [ 3 ] )
writer : Execute ( image ) ;
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Python
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import SimpleITK as sitk
import sys
if len ( sys . argv ) < 4 :
print " Usage : Sim pleGauss ian < input > < sigma > < output > " ;
sys . exit ( 1 )
reader = sitk . I ma ge Fi l eR ea de r ( )
reader . SetFileName ( sys . argv [ 1 ] )
image = reader . Execute ( )
pixelID = image . G et Pi xe l ID Va lu e ( )
gaussian = sitk . S m o o t h i n g R e c u r s i v e G a u s s i a n I m a g e F i l t e r ( )
gaussian . SetSigma ( float ( sys . argv [ 2 ] ) )
image = gaussian . Execute ( image )
caster = sitk . C as tI ma g eF il te r ( )
caster . S e t O u t p u t P i x e l T y p e ( pixelID )
image = caster . Execute ( image )
writer = sitk . I ma ge Fi l eW ri te r ( )
writer . SetFileName ( sys . argv [ 3 ] )
writer . Execute ( image ) ;
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Tcl
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if { $argc < 3 } {
puts " Usage: Simpl eGaussia n < input > < sigma > < output > "
exit 1
}
I ma ge Fi l eR ea de r reader
reader SetFileName [ lindex $argv 0 ]
set image [ reader Execute ]
set pixelID [ $image G et P ix el ID V al ue ]
S m o o t h i n g R e c u r s i v e G a u s s i a n I m a g e F i l t e r gaussian
gaussian SetSigma [ lindex $argv 1 ]
set image [ gaussian Execute $image ]
Ca s tI ma ge F il te r caster
caster S e t O u t p u t P i x e l T y p e $pixelID
set image [ caster Execute $image ]
Im a ge Fi le W ri te r writer
writer SetFileName [ lindex $argv 2 ]
writer Execute $image
# Tcl r e q u i r e s e x p l i c i t cleanup Cleanup
reader -delete
gaussian -delete
caster -delete
$image -delete
writer -delete
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Conclusion
Conclusion
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Wrap-up
Gentle introduction to ITK
Introduce SimpleITK
Provide hands-on experience
Problem solving, not direction following
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Where to go next
Some resources for using and extending SimpleITK
Home Page
http://www.simpleitk.org
Documentation
http://www.itk.org/SimpleITKDoxygen/html/
Conventions
http:
//www.itk.org/SimpleITKDoxygen/html/Conventions.html
Contributions
http://www.itk.org/SimpleITKDoxygen/html/Developer.html
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SimpleITK Tutorial

Transcription

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