A study about the implementation of the integrated ITK/VTK environment for neuroimage analysis

Abstract

National Library of Medicine을 기반으로 개발된 ITK(Insight Toolkit)와 VTK(Visualization Toolkit)는 Kitware에서 제공하는 오픈 소스 영상처리 시스템으로 ITK는 주로 영상자동분할(segmentation)과 영상정합(registration)에 관련된 다양한 라이브러리로 구성되어 있으며 VTK는 주로 이미지 시각화에 관련된 라이브러리로 구성되어 있다. 전반적인 구조를 살펴보면 ITK와 VTK 모두 핵심 컴파일 언어로는 C++이 사용되었으며 Tcl/Tk, Java, Python 등 여러 다른 언어로 사용할 수 있도록 wrapping 되어 있다. 이렇게 각각 독립적으로 개발되어 있는 ITK와 VTK를 하나의 모듈로 통합하여 사용함으로써 이미지 처리 시간을 단축할 수 있고 영상처리 결과뿐만 아니라 그 과정을 시각화 함으로써 정확도 있는 결과를 도출할 수 있는 장점이 있다. 본 연구에서는 ITK2.6.0, VTK5.0.0, 그리고 CMake2.4를 사용하여 ITK/VTK 통합 알고리즘을 구현하였으며 이를 뇌 영상 segmentation에 활용하였으며 그 결과 skull-stripping된 각각의 이미지들이 통합 View System을 통하여 Display되었다.; The Insight Toolkit (ITK) initiative from the National Library of Medicine has provided a suite of state-of-the-art segmentation and registration algorithms ideally suited to volume visualization and analysis. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Because ITK is an open-source project, developers from around the world can use, debug, maintain, and extend the software. The most recent version of this document and source is available online at http://www.itk.org. The visualization ToolKit (VTK) is an open source, freely available software system for 3D computer graphics, image processing, and visualization. VTK has been implemented on nearly every Unix-based platform, PC's (Windows 95/98/NT/2000/XP) and Mac OSX Jaguar and later. The design and implementation of the library has been strongly influenced by object-oriented principles. The graphics model in VTK is at a higher level of abstraction than rendering libraries like OpenGL or PEX. This means it is much easier to create useful graphics and visualization applications. In VTK applications can be written directly in C++, Tcl, Java, or Python. In fact, using the interpreted languages Tcl or Python with Tk, and even Java with its GUI class libraries, it is possible to build useful applications really, really fast. Finally, VTK supports a wide variety of visualization algorithms including scalar, vector, tensor, texture, and volumetric methods; and advanced modeling techniques like implicit modelling, polygon reduction, mesh smoothing, cutting, contouring, and Delaunay triangulation. TK and VTK is implemented in C++. It is cross-platform, using a build environment known as CMake to manage the compilation process in a platform-independent way. In this paper I described how to implement of the integrated ITK/VTK algorithm for neuroimage segmentation. Integration of ITK/VTK is quite difficult process because ITK and VTK is generally no interaction each other and implemented independently. As a kind of application I tested the algorithm on ten sets of brain MR images and evaluated the results against gold standard.