itk教程，详解入门级通俗易懂.pdf

发布时间：2022-06-13 发布人：admin 分类：说明书资料大小：5.18M 资料格式：pdf 举报版权申诉

572851e5-79e9-4609-b7f5-0d2d37077247.pdf-第1页.png

第1页 / 共768页

572851e5-79e9-4609-b7f5-0d2d37077247.pdf-第2页.png

第2页 / 共768页

572851e5-79e9-4609-b7f5-0d2d37077247.pdf-第3页.png

第3页 / 共768页

572851e5-79e9-4609-b7f5-0d2d37077247.pdf-第4页.png

第4页 / 共768页

572851e5-79e9-4609-b7f5-0d2d37077247.pdf-第5页.png

第5页 / 共768页

572851e5-79e9-4609-b7f5-0d2d37077247.pdf-第6页.png

第6页 / 共768页

572851e5-79e9-4609-b7f5-0d2d37077247.pdf-第7页.png

第7页 / 共768页

572851e5-79e9-4609-b7f5-0d2d37077247.pdf-第8页.png

第8页 / 共768页

I Introduction

Welcome

Organization

How to Learn ITK

Software Organization

Obtaining the Software

Downloading ITK

Downloading Packaged Releases

Downloading using Git

Join the Mailing List

Directory Structure

Documentation

Data

The Insight Community and Support

A Brief History of ITK

Installation

Configuring ITK

Preparing CMake

Configuring ITK

Getting Started With ITK

Hello World !

System Overview

System Organization

Essential System Concepts

Generic Programming

Include Files and Class Definitions

Object Factories

Smart Pointers and Memory Management

Error Handling and Exceptions

Event Handling

Multi-Threading

Numerics

Data Representation

Data Processing Pipeline

Spatial Objects

Wrapping

II Design and Functionality

DataRepresentation

Image

Creating an Image

Reading an Image from a File

Accessing Pixel Data

Defining Origin and Spacing

RGB Images

Vector Images

Importing Image Data from a Buffer

PointSet

Creating a PointSet

Getting Access to Points

Getting Access to Data in Points

RGB as Pixel Type

Vectors as Pixel Type

Normals as Pixel Type

Mesh

Creating a Mesh

Inserting Cells

Managing Data in Cells

Customizing the Mesh

Topology and the K-Complex

Representing a PolyLine

Simplifying Mesh Creation

Iterating Through Cells

Visiting Cells

More on Visiting Cells

Path

Creating a PolyLineParametricPath

Containers

Spatial Objects

Introduction

Hierarchy

SpatialObject Tree Container

Transformations

Types of Spatial Objects

ArrowSpatialObject

BlobSpatialObject

CylinderSpatialObject

EllipseSpatialObject

GaussianSpatialObject

GroupSpatialObject

ImageSpatialObject

ImageMaskSpatialObject

LandmarkSpatialObject

LineSpatialObject

MeshSpatialObject

SurfaceSpatialObject

TubeSpatialObject

VesselTubeSpatialObject

DTITubeSpatialObject

SceneSpatialObject

Read/Write SpatialObjects

Statistics Computation via SpatialObjects

Filtering

Thresholding

Binary Thresholding

General Thresholding

Edge Detection

Canny Edge Detection

Casting and Intensity Mapping

Linear Mappings

Non Linear Mappings

Gradients

Gradient Magnitude

Gradient Magnitude With Smoothing

Derivative Without Smoothing

Second Order Derivatives

Second Order Recursive Gaussian

Laplacian Filters

Laplacian Filter Finite Difference

Laplacian Filter Recursive Gaussian

Neighborhood Filters

Mean Filter

Median Filter

Mathematical Morphology

Binary Filters

Grayscale Filters

Voting Filters

Binary Median Filter

Hole Filling Filter

Iterative Hole Filling Filter

Smoothing Filters

Blurring

Discrete Gaussian

Binomial Blurring

Recursive Gaussian IIR

Local Blurring

Gaussian Blur Image Function

Edge Preserving Smoothing

Introduction to Anisotropic Diffusion

Gradient Anisotropic Diffusion

Curvature Anisotropic Diffusion

Curvature Flow

MinMaxCurvature Flow

Bilateral Filter

Edge Preserving Smoothing in Vector Images

Vector Gradient Anisotropic Diffusion

Vector Curvature Anisotropic Diffusion

Edge Preserving Smoothing in Color Images

Gradient Anisotropic Diffusion

Curvature Anisotropic Diffusion

Distance Map

Geometric Transformations

Filters You Should be Afraid to Use

Change Information Image Filter

Flip Image Filter

Resample Image Filter

Introduction

Importance of Spacing and Origin

A Complete Example

Rotating an Image

Rotating and Scaling an Image

Resampling using a deformation field

Subsampling and image in the same space

Resampling an Anisotropic image to make it Isotropic

Frequency Domain

Computing a Fast Fourier Transform (FFT)

Filtering on the Frequency Domain

Extracting Surfaces

Surface extraction

Reading and Writing Images

Basic Example

Pluggable Factories

Using ImageIO Classes Explicitly

Reading and Writing RGB Images

Reading, Casting and Writing Images

Extracting Regions

Extracting Slices

Reading and Writing Vector Images

The Minimal Example

Producing and Writing Covariant Images

Reading Covariant Images

Reading and Writing Complex Images

Extracting Components from Vector Images

Reading and Writing Image Series

Reading Image Series

Writing Image Series

Reading and Writing Series of RGB Images

Reading and Writing DICOM Images

Foreword

Reading and Writing a 2D Image

Reading a 2D DICOM Series and Writing a Volume

Reading a 2D DICOM Series and Writing a 2D DICOM Series

Printing DICOM Tags From One Slice

Printing DICOM Tags From a Series

Changing a DICOM Header

Registration

Registration Framework

"Hello World" Registration

Features of the Registration Framework

Direction of the Transform Mapping

Registration is done in physical space

Monitoring Registration

Multi-Modality Registration

Viola-Wells Mutual Information

Mattes Mutual Information

Centered Transforms

Rigid Registration in 2D

Initializing with Image Moments

Similarity Transform in 2D

Rigid Transform in 3D

Centered Affine Transform

Multi-Resolution Registration

Fundamentals

Parameter Tuning

Transforms

Geometrical Representation

Transform General Properties

Identity Transform

Translation Transform

Scale Transform

Scale Logarithmic Transform

Euler2DTransform

CenteredRigid2DTransform

Similarity2DTransform

QuaternionRigidTransform

VersorTransform

VersorRigid3DTransform

Euler3DTransform

Similarity3DTransform

Rigid3DPerspectiveTransform

AffineTransform

BSplineDeformableTransform

KernelTransforms

Interpolators

Nearest Neighbor Interpolation

Linear Interpolation

B-Spline Interpolation

Windowed Sinc Interpolation

Metrics

Mean Squares Metric

Exploring a Metric

Normalized Correlation Metric

Mean Reciprocal Square Differences

Mutual Information Metric

Parzen Windowing

Viola and Wells Implementation

Mattes et al. Implementation

Kullback-Leibler distance metric

Normalized Mutual Information Metric

Mean Squares Histogram

Correlation Coefficient Histogram

Cardinality Match Metric

Kappa Statistics Metric

Gradient Difference Metric

Optimizers

Registration using Match Cardinality metric

Registration using the One plus One Evolutionary Optimizer

Registration using masks constructed with Spatial objects

Rigid registrations incorporating prior knowledge

Image Pyramids

Deformable Registration

FEM-Based Image Registration

BSplines Image Registration

Level Set Motion for Deformable Registration

BSplines Multi-Grid Image Registration

BSplines Image Registration in 3D

Image Warping with Kernel Splines

Image Warping with BSplines

Demons Deformable Registration

Asymmetrical Demons Deformable Registration

Symmetrical Demons Deformable Registration

Visualizing Deformation fields

Visualizing 2D deformation fields

Visualizing 3D deformation fields

Model Based Registration

Point Set Registration

Point Set Registration in 2D

Point Set Registration in 3D

Point Set to Distance Map Metric

Registration Troubleshooting

Too many samples outside moving image buffer

General heuristics for parameter fine-tunning

Segmentation

Region Growing

Connected Threshold

Otsu Segmentation

Neighborhood Connected

Confidence Connected

Application of the Confidence Connected filter on the Brain Web Data

Isolated Connected

Confidence Connected in Vector Images

Segmentation Based on Watersheds

Overview

Using the ITK Watershed Filter

Level Set Segmentation

Fast Marching Segmentation

Shape Detection Segmentation

Geodesic Active Contours Segmentation

Threshold Level Set Segmentation

Canny-Edge Level Set Segmentation

Laplacian Level Set Segmentation

Geodesic Active Contours Segmentation With Shape Guidance

Feature Extraction

Hough Transform

Line Extraction

Circle Extraction

Statistics

Data Containers

Sample Interface

Sample Adaptors

ImageToListSampleAdaptor

PointSetToListSampleAdaptor

Histogram

Subsample

MembershipSample

MembershipSampleGenerator

K-d Tree

Algorithms and Functions

Sample Statistics

Mean and Covariance

Weighted Mean and Covariance

Sample Generation

SampleToHistogramFilter

NeighborhoodSampler

Sample Sorting

Probability Density Functions

Gaussian Distribution

Distance Metric

Euclidean Distance

Decision Rules

Maximum Decision Rule

Minimum Decision Rule

Maximum Ratio Decision Rule

Random Variable Generation

Normal (Gaussian) Distribution

Statistics applied to Images

Image Histograms

Scalar Image Histogram with Adaptor

Scalar Image Histogram with Generator

Color Image Histogram with Generator

Color Image Histogram Writing

Image Information Theory

Computing Image Entropy

Computing Images Mutual Information

Classification

k-d Tree Based k-Means Clustering

K-Means Classification

Bayesian Plug-In Classifier

Expectation Maximization Mixture Model Estimation

Classification using Markov Random Field

III ITK Community Participation

Iterators

Introduction

Programming Interface

Creating Iterators

Moving Iterators

Accessing Data

Iteration Loops

Image Iterators

ImageRegionIterator

ImageRegionIteratorWithIndex

ImageLinearIteratorWithIndex

ImageSliceIteratorWithIndex

ImageRandomConstIteratorWithIndex

Neighborhood Iterators

NeighborhoodIterator

Basic neighborhood techniques: edge detection

Convolution filtering: Sobel operator

Optimizing iteration speed

Separable convolution: Gaussian filtering

Slicing the neighborhood

Random access iteration

ShapedNeighborhoodIterator

Shaped neighborhoods: morphological operations

Image Adaptors

Image Casting

Adapting RGB Images

Adapting Vector Images

Adaptors for Simple Computation

Adaptors and Writers

How To Write A Filter

Terminology

Overview of Filter Creation

Streaming Large Data

Overview of Pipeline Execution

Details of Pipeline Execution

UpdateOutputInformation()

PropagateRequestedRegion()

UpdateOutputData()

Threaded Filter Execution

Filter Conventions

Optional

Useful Macros

How To Write A Composite Filter

Implementing a Composite Filter

A Simple Example

Software Process

Git Source Code Repository

CDash Regression Testing System

Working The Process

The Effectiveness of the Process

Appendices

Licenses

Insight Toolkit License

Third Party Licenses

DICOM Parser

Double Conversion

Expat

GDCM

GIFTI

HDF5

JPEG

KWSys

MetaIO

Netlib's SLATEC

NIFTI

NrrdIO

OpenJPEG

PNG

TIFF

VNL

ZLIB

The ITK Software Guide Third Edition Updated for ITK version 4.5 Hans J. Johnson, Matt McCormick, Luis Ib ´a ˜nez, and the Insight Software Consortium December 17, 2013 http://itk.org Email: community@itk.org

The purpose of computing is Insight, not numbers. Richard Hamming

ABSTRACT The Insight Toolkit (ITK) is an open-source software toolkit for performing registration and segmentation. Segmentation is the process of identifying and classifying data found in a digi- tally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or de- veloping correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both. ITK is a cross-platform software. It uses a build environment known as CMake to manage platform-speciﬁc project generation and compilation process in a platform-independent way. ITK is implemented in C++. ITK’s implementation style employs generic programming, which involves the use of templates to generate, at compile-time, code that can be applied generically to any class or data-type that supports the operations used by the template. The use of C++ templating means that the code is highly efﬁcient and many issues are discovered at compile- time, rather than at run-time during program execution. It also means that many of ITK’s algorithms can be applied to arbitrary spatial dimensions and pixel types. An automated wrapping system integrated with ITK generates an interface between C++ and a high-level programming language Python. This enables rapid prototyping and faster exploration of ideas by shortening the edit-compile-execute cycle. In addition to automated wrapping, the SimpleITK project provides a streamlined interface to ITK that is available for C++, Python, Java, CSharp, R, Tcl and Ruby. Developers from around the world can use, debug, maintain, and extend the software because ITK is an open-source project. ITK uses a model of software development known as Extreme Programming. Extreme Programming collapses the usual software development methodology into a simultaneous iterative process of design-implement-test-release. The key features of Extreme Programming are communication and testing. Communication among the members of the ITK community is what helps manage the rapid evolution of the software. Testing is what keeps the software stable. An extensive testing process supported by the system known as CDash measures the quality of ITK code on a daily basis. The ITK Testing Dashboard is

updated continuously, reﬂecting the quality of the code at any moment. This book is a guide to developing software with ITK. It covers building and installation, ar- chitecture and design, image analysis theory and its applications, as well as the process of contributing to the ITK community. The most http://itk.org/ItkSoftwareGuide.pdf. version recent of this document is available online at

CONTRIBUTORS The Insight Toolkit (ITK) has been created by the efforts of many talented individuals and prestigious organizations. It is also due in great part to the vision of the program established by Dr. Terry Yoo and Dr. Michael Ackerman at the National Library of Medicine. This book lists a few of these contributors in the following paragraphs. Not all developers of ITK are credited here, so please visit the Web pages at http://www.itk.org/HTML/About.htm for the names of additional contributors, as well as checking the GIT source logs for code contributions. The following is a brief description of the contributors to this software guide and their contri- butions. Luis Ib´a ˜nez is principal author of this text. He assisted in the design and layout of the text, implemented the bulk of the LATEX and CMake build process, and was responsible for the bulk of the content. He also developed most of the example code found in the Insight/Examples directory. Will Schroeder helped design and establish the organization of this text and the Insight/Examples directory. He is principal content editor, and has authored several chap- ters. Lydia Ng authored the description for the registration framework and its components, the sec- tion on the multiresolution framework, and the section on deformable registration methods. She also edited the section on the resampling image ﬁlter and the sections on various level set segmentation algorithms. Joshua Cates authored the iterators chapter and the text and examples describing watershed segmentation. He also co-authored the level-set segmentation material. Jisung Kim authored the chapter on the statistics framework. Julien Jomier contributed the chapter on spatial objects and examples on model-based regis- tration using spatial objects.

vi Karthik Krishnan reconﬁgured the process for automatically generating images from all the examples. Added a large number of new examples and updated the Filtering and Segmentation chapters for the second edition. Stephen Aylward contributed material describing spatial objects and their application. Tessa Sundaram contributed the section on deformable registration using the ﬁnite element method. YinPeng Jin contributed the examples on hybrid segmentation methods. Celina Imielinska authored the section describing the principles of hybrid segmentation meth- ods. Mark Foskey contributed the examples on the AutomaticTopologyMeshSource class. Mathieu Malaterre contributed the entire section on the description and use of DICOM readers and writers based on the GDCM library. He also contributed an example on the use of the VTKImageIO class. Gavin Baker contributed the section on how to write composite ﬁlters. Also known as minip- ipeline ﬁlters. Since the software guide is generated in part from the ITK source code itself, many ITK de- velopers have been involved in updating and extending the ITK documentation. These in- clude David Doria, Bradley Lowecamp, Mark Foskey, Ga¨etan Lehmann, Andreas Schuh, Tom Vercauteren, Cory Quammen, Daniel Blezek, Paul Hughett, Matt McCormick, Josh Cates, Arnaud Gelas, Jim Miller, Brad King, Gabe Hart, Hans Johnson. Hans Johnson, Kent Williams, Constantine Zakkaroff, Xiaoxiao Liu and Matt McCormick updated the documentation for the initial ITK Version 4 release.

分享到：

赞收藏

资料库

itk教程，详解入门级通俗易懂.pdf

相关推荐

课程资源

热门标签

最新资料