KEYWORDS: Image segmentation, 3D modeling, Lung, Magnetic resonance imaging, Data modeling, Medical imaging, 3D image processing, Heart, Process modeling, Image processing
Medical image segmentation is still a very time consuming task and therefore not often integrated into clinical routine. Various 3D segmentation approaches promise to facilitate the work. But they are rarely used in clinical setups due to complex intialization and parametrization of such models. Clinical users need interactive tools, intuitive and easy to handle. They do not want to play around with a set of parameters which will differ from dataset to dataset and often have a non-intuitive meaning. In this work new interactive constraints for deformable three-dimensional 2-simplex meshes are presented. The user can define attracting points in the original image data. These attractors are considered during model deformation and the new forces guarantee that the surface model will pass through these interactively set points. By using the constraints the model parameterization is simplified. Segmentation is started with a spherical surface model which is placed inside the structure of interest and then adapts to the boundaries. The user can directly influence the evolution of the deformable model and gets direct feedback during the segmentation process. The model deformation algorithm was implemented and integrated in ITK (Insight Segmentation and Registration Toolkit). The newly developed segmentation tool was tested on cardiac image data and MRI lung images, but is suitable for any kind of 3D and 3D+t medical image data. It has been shown that the model is less sensitive to preprocessing of the input data as well as model initialization.
KEYWORDS: Visualization, Medical imaging, Image segmentation, Surgery, 3D vision, Data acquisition, 3D image processing, Image processing, Image processing algorithms and systems, Data processing
The aim of the Medical Imaging Interaction Toolkit (MITK) is to facilitate the creation of clinically usable
image-based software. Clinically usable software for image-guided procedures and image analysis require a high
degree of interaction to verify and, if necessary, correct results from (semi-)automatic algorithms. MITK is
a class library basing on and extending the Insight Toolkit (ITK) and the Visualization Toolkit (VTK). ITK
provides leading-edge registration and segmentation algorithms and forms the algorithmic basis. VTK has
powerful visualization capabilities, but only low-level support for interaction (like picking methods, rotation,
movement and scaling of objects). MITK adds support for high level interactions with data like, for example, the
interactive construction and modification of data objects. This includes concepts for interactions with multiple
states as well as undo-capabilities. Furthermore, VTK is designed to create one kind of view on the data
(either one 2D visualization or a 3D visualization). MITK facilitates the realization of multiple, different views
on the same data (like multiple, multiplanar reconstructions and a 3D rendering). Hierarchically structured
combinations of any number and type of data objects (image, surface, vessels, etc.) are possible. MITK can
handle 3D+t data, which are required for several important medical applications, whereas VTK alone supports
only 2D and 3D data. The benefit of MITK is that it supplements those features to ITK and VTK that are
required for convenient to use, interactive and by that clinically usable image-based software, and that are
outside the scope of both. MITK will be made open-source (http://www.mitk.org).
Computer-assisted surgery aims at a decreased surgical risk and a reduced recovery time of patients. However, its use is still limited to complex cases because of the high effort. It is often caused by the extensive medical image analysis. Especially, image segmentation requires a lot of manual work. Surgeons and radiologists are suffering from usability problems of many workstations.
In this work, we present a dedicated workplace for interactive segmentation integratd within the CHILI (tele-)radiology system. The software comes with a lot of improvements with respect to its graphical user interface, the segmentation process and the segmentatin methods. We point out important software requirements and give insight into the concepts which were implemented. Further examples and applications illustrate the software system.
For diagnostics and therapy planning, the segmentation of medical images is an important pre-processing step. Currently, manual segmentation tools are most common in clinical routine. Because the work is very time-consuming, there is a large interest in tools assisting the physician. Most of the known segmentation techniques suffer from an inadequate user interface, which prevents their use in a clinical environment. The segmentation of medical images is very difficult. A promising method to overcome difficulties such as imaging artifacts are active contour models. In order to enhance the clinical usability, we propose a user-driven segmentation approach. Following this way, we developed a new segmentation method, which we call interactive snakes. Thereto, we elaborated an interaction style which is more intuitive to the clinical user and derived a new active contour model. The segmentation method provides a very tight coupling with the user. The physician is interactively attaching boundary markers to the image, whereby he is able to bring in his knowledge. At the same time, the segmentation is updated in real-time. Interactive snakes are a comprehensible segmentation method for the clinical use. It is reasonable to employ them both as a core tool and as an editing tool for incorrect results.
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