The automatic segmentation of relevant structures such as skin edge, chest wall, or nipple in dynamic contrast
enhanced MR imaging (DCE MRI) of the breast provides additional information for computer aided diagnosis (CAD) systems. Automatic reporting using BI-RADS criteria benefits of information about location of those
structures. Lesion positions can be automatically described relatively to such reference structures for reporting
purposes. Furthermore, this information can assist data reduction for computation expensive preprocessing such
as registration, or for visualization of only the segments of current interest. In this paper, a novel automatic method for determining the air-breast boundary resp. skin edge, for approximation of the chest wall, and locating of the nipples is presented. The method consists of several steps which are built on top of each other. Automatic threshold computation leads to the air-breast boundary which is then analyzed to determine the location of the nipple. Finally, results of both steps are starting point for approximation of the chest wall. The proposed process was evaluated on a large data set of DCE MRI recorded by T1 sequences and yielded reasonable results in all cases.
Visualization and image processing of medical datasets has become an essential task for clinical diagnosis support as well as for treatment planning. In order to enable a physician to use and evaluate algorithms within a clinical setting, easily applicable software prototypes with a dedicated user interface are essential. However, substantial programming knowledge is still required today when using powerful open source libraries such as the Visualization Toolkit (VTK) or the Insight Toolkit (ITK). Moreover, these toolkits provide only limited graphical user interface functionality. In this paper, we present the visual programming and rapid prototyping platform MeVisLab which provides flexible and simple handling of visualization and image processing algorithms of VTK/ITK, Open Inventor and the MeVis Image Library by modular visual programming. No programming knowledge is required to set up image processing and visualization pipelines. Complete applications including user interfaces can be easily built within a general framework. In addition to the VTK/ITK features, MeVisLab provides a full integration of the Open Inventor library and offers a state-of-the-art integrated volume renderer. The integration of VTK/ITK algorithms is performed automatically: an XML structure is created from the toolkits' source code followed by an automatic module generation from this XML description. Thus, MeVisLab offers a one stop solution integrating VTK/ITK as modules and is suited for rapid prototyping as well as for teaching medical visualization and image analysis. The VTK/ITK integration is available as package of the free version of MeVisLab.
In MR mammography, usually the complete upper part of the body is recorded although for most diagnostic examinations and therapeutic planning only the regions around the breasts are important. This can lead to some disadvantages for automatic processing of images, e.g. higher time consumption and lesser accuracy in image registration. In this paper, we present a straight-forward method for automatic cropping of breast regions for registration in MR mammography. The method starts with processing two-dimensional slices. The result of this first step is statistically analyzed and a cropping-region for three-dimensional volumes is calculated. For each two-dimensional slice, the boundary between breast and air is identified by applying a threshold operator. This boundary describes a bimodal curve; the peaks for the right and left breast and the breastbone are found by searching among the curve's extremal points. An heuristic method analyzes this curve and further yields an estimation of the chest wall boundary to the inner body. For image registration purpose, we compare our proposed automatic cropping method with a simple cropping mechanism that defines the regions of the left and right breasts by reducing the volume to 60 percent from the margin of each side. It is shown that the proposed method works reliably and gives advantages regarding the time-quality tradeoff for automatic image registration in MR mammography.
The synthesis of holograms by computer requires the calculation of the complex amplitude emitted by an object in the hologram plane. Several approaches exist that decompose the object into primitives like points and lines. We assume that the object to be imaged can be approximately decomposed into congruent triangles. In a preprocessing step, we calculate wavefields for the triangle whose transformed copies build the object surface. The computed wavefields represent triangles rotated by different angles and positioned in different depths. The resulting wavefields are stored as conventional color images with alpha channel in a lookup table indexed by rotation angle and distance from the hologram plane. Each pixel in the texture codes a complex number. Every triangle of the input object has a corresponding entry in the lookup table. The rotation angles and the distance of the triangle determine the selection of the appropriate texture. The textures are rendered using special graphics hardware, and interference is simulated. The lookup table helps to react immediately to transformations of the input object. Texture swapping and repositioning according to the object movings lead to full- parallax hologram generation for small objects in real-time.
Synthetic holography simulates both the optical recording and reconstruction by computer. In this paper we describe several visualization methods for synthetic reconstruction of holograms. Such reconstructions are used in combination with computer generated holograms for verification purposes and also for the exploration of optically recorded holograms. To visualize a reconstruction we calculate the emitted wave field for several planes in different distances to the hologram by applying wave propagation operators. The planes are combined to a volumetric data set. This data set can be expressed as a complex valued function of three independent variables. An appropriate visualization technique is volume rendering of the function values. Opacity and intensity of the generated voxels are proportionally weighted to the intensity of the given data set. This representation approaches the underlying physical process. Another method is iso surface generation for the intensities of the function values which leads to approximations of focal points of the object coded in the hologram. Applications of our methods are given and examples are discussed.