Systematic Parameterization, Storage, and Representation of Volumetric DICOM Data.

Tomographic medical imaging systems produce hundreds to thousands of slices, enabling three-dimensional (3D) analysis. Radiologists process these images through various tools and techniques in order to generate 3D renderings for various applications, such as surgical planning, medical education, and volumetric measurements. To save and store these visualizations, current systems use snapshots or video exporting, which prevents further optimizations and requires the storage of significant additional data. The Grayscale Softcopy Presentation State extension of the Digital Imaging and Communications in Medicine (DICOM) standard resolves this issue for two-dimensional (2D) data by introducing an extensive set of parameters, namely 2D Presentation States (2DPR), that describe how an image should be displayed. 2DPR allows storing these parameters instead of storing parameter applied images, which cause unnecessary duplication of the image data. Since there is currently no corresponding extension for 3D data, in this study, a DICOM-compliant object called 3D presentation states (3DPR) is proposed for the parameterization and storage of 3D medical volumes. To accomplish this, the 3D medical visualization process is divided into four tasks, namely pre-processing, segmentation, post-processing, and rendering. The important parameters of each task are determined. Special focus is given to the compression of segmented data, parameterization of the rendering process, and DICOM-compliant implementation of the 3DPR object. The use of 3DPR was tested in a radiology department on three clinical cases, which require multiple segmentations and visualizations during the workflow of radiologists. The results show that 3DPR can effectively simplify the workload of physicians by directly regenerating 3D renderings without repeating intermediate tasks, increase efficiency by preserving all user interactions, and provide efficient storage as well as transfer of visualized data.

A software tool for interactive generation, representation, and systematical storage of transfer functions for 3D medical images.

As being a tool that assigns optical parameters, i.e. color, transparency, used in interactive visualization, transfer functions have very important effects on the quality of volume rendered medical images. However, finding accurate transfer functions is a very difficult, tedious, and time consuming task because of the variety of all possibilities. By addressing this problem, a software module, which can be easily plugged into any visualization program, is developed based on the specific expectations of medical experts. Its design includes both a new user interface to ease the interactive generation of the volume rendered medical images and a volumetric histogram based method for initial generation of transfer functions. In addition, a novel file system has been implemented to represent 3D medical images using transfer functions based on the DICOM standard. For evaluation of the system by various medical experts, the software is installed into a DICOM viewer. Based on the feedback obtained from the medical experts, several improvements are made, especially to increase the flexibility of the program. The final version of the implemented system shortens the transfer function design process and is applicable to various application areas.

Semi-automatic segmentation methods for 3-D visualization and analysis of the liver.

Pre-evaluation of donors prior to surgery of living donated liver transplantation is one of the challenging applications that computer aided systems are needed. The precise measurement of liver volume requires effective segmentation procedures, while three dimensional rendering of the segmented data provides demonstrative information to radiologists and surgeons before surgery. The Insight Toolkit provides effective algorithms for segmentation, which are also optimized for high computational performance and processing time. Furthermore, An ITK pipeline can be combined with a VTK pipeline, so that the result of segmentation can be represented directly in 3-D using VTK. Therefore, there is an on-going trend for developing ITK/VTK based systems. This study presents quantitative and qualitative performance evaluation of two effective ITK algorithms on segmentation of liver from CTA data sets.

Performance comparison of compression algorithms for archiving segmented volumetric binary medical data.

Archiving result of a segmentation task allows the representation of the segmented volume at a later time. The segmented volume can be stored in a binary format, which can be restored by a simple combination of the original data with this binary information. Since, the sizes of the segmented binary data have high memory requirements; a lossless compression method should be employed for efficient archiving. Thus, this study examines different approaches for compression and their suitability for restoring binary segmentation results. To evaluate the compressive properties, multiple test cases with diverse spatial structures and acquired with different modalities from clinical practice have been used. The results show that best performance is achieved with JBIG2 method both in terms of compression ratio and processing time.

Integrating segmentation methods from different tools into a visualization program using an object-based plug-in interface.

In medical visualization, segmentation is an important step prior to rendering. However, it is also a difficult procedure because of the restrictions imposed by variations in image characteristics, human anatomy, and pathology. Moreover, what is interesting from clinical point of view is usually not only an organ or a tissue itself, but also its properties together with adjacent organs or related vessel systems that are going in and coming out. For an informative rendering, these necessitate the usage of different segmentation methods in a single application, and combining/representing the results together in a proper way. This paper describes the implementation of an interface, which can be used to plug-in and then apply a segmentation method to a medical image series. The design is based on handling each segmentation procedure as an object where all parameters of each object can be specified individually. Thus, it is possible to use different plug-ins with different interfaces and parameters for the segmentation of different tissues in the same dataset while rendering all of the results together is still possible. The design allows access to insight registration and segmentation toolkit, Java, and MATLAB functionality together, eases sharing and comparing segmentation techniques, and serves as a visual debugger for algorithm developers.