Digital scan conversion algorithm is the most computational intensive part of B-mode ultrasound imaging. Traditionally,
in order to meet the requirements of real-time imaging, digital scan conversion algorithm often traded off image quality
for speed, such as the use of simple image interpolation algorithm, the use of look-up table to carry out polar coordinates
transform and logarithmic compression. This paper presents a GPU-based high-definition real-time ultrasound digital
scan conversion algorithm implementation. By rendering appropriate proxy geometry, we can implement a high
precision digital scan conversion pipeline, including polar coordinates transform, bi-cubic image interpolation, high
dynamic range tone reduction, line average and frame persistence FIR filtering, 2D post filtering, fully in the fragment
shader of GPU at real-time speed. The proposed method shows the possibility of updating exist FPGA or ASIC based
digital scan conversion implementation to low cost GPU based high-definition digital scan conversion implementation.
Three-dimensional human computer interaction plays an important role in 3-dimensional visualization. It is important for clinicians to accurately use and easily handle the result of medical data visualization in order to assist diagnosis and surgery simulation. A 3D human computer interaction software platform based on 3D widgets has been designed in traditional object-oriented fashion with some common design patterns and implemented by using ANSI C++, including all function modules and some practical widgets. A group of application examples are exhibited as well. The ultimate objective is to provide a flexible, reliable and extensible 3-D interaction platform for medical image processing and analyzing.
Proc. SPIE. 5367, Medical Imaging 2004: Visualization, Image-Guided Procedures, and Display
KEYWORDS: Image processing algorithms and systems, Detection and tracking algorithms, Data modeling, Visualization, Image processing, Image analysis, Medical imaging, Volume rendering, Reconstruction algorithms, C++
With the success of VTK and ITK, there are more attentions to the toolkit development issue in medical imaging
community. This paper introduces MITK, an integrated medical image processing and analyzing toolkit. Its main
purpose is to provide a consistent framework to combine the function of medical image segmentation, registration and
visualization. The design goals, overall framework and implementation of some key technologies are provided in
details, and some application examples are also given to demonstrate the ability of MITK. We hope that MITK will
become another available choice for the medical imaging community.
In this paper a practical surface reconstruction algorithm is proposed to efficiently process very large medical dataset in general PC. By considering the conflict between memory consumption and traversal speed, we restrict the traditional surface tracking in single layer and thus get a better trade-off between them. We also use a compression scheme to store the generated mesh, which decrease the memory requirement considerably. For efficient rendering, we employ a triangle strips generation algorithm to decode directly the com-pressed mesh into triangle strip. The experimental results tested on visible man fresh CT dataset show that the proposed algorithm is very efficient in both extracting and rendering phase.
Virtual endoscopy is meaningful for medical diagnosis and surgery. In this paper, a system framework for virtual endoscopy is proposed including automatic centerline extraction and view-dependent level-of-detail rendering techniques. Combining Hessian Matrix with distance mapping, our path planning method can generate accurate skeleton for virtual navigation. Furthermore real tim rendering can be achieved with our new view-dependent subdivision algorithm. The experimental results show the efficiency of our methods.
An integrated 3D medical image processing and analysis system we developed can provide powerful functions such as image preprocessing, virtual cutting, surface rendering, volume rendering, and manipulation. The system description, the method adopted and the application examples are presented. The system can be widely applied to processing and analysis of CT and MR images.
With the increasing of medical image datasets, the 3D model obtained by reconstruction often incorporates millions of triangles that make real time rendering very difficult. Progressive Mesh (PM) had been developed to address the above problem of view-dependent level-of-detail control, but its speed can’t meet the requirement of virtual endoscopy. In this study, we developed a new view-dependent continuous level-of-detail (CLOD) algorithm for triangle meshes with subdivision connectivity. First, the mesh was simplified in hierarchy to get the simplest mesh (called as base domain), then each hierarchy of the simplified mesh was parameterized to map to the base domain, and finally the view-dependent subdivision was used to resample the mesh to get a multi-resolution model. We constructed an index to record the changes of view parameters by the adaptive octree so as to make full use of the reusability of the adjacent frame and reduce the dynamic changes of the selected levels of detail. We tested our algorithm in several different datasets. The experiments showed that our method is efficient and easy to implement, and the model can be rendered in real time to meet the requirement of virtual endoscopy.
In the field of medical imaging, researchers often need visualize lots of 3D datasets to get the informaiton contained in these datasets. But the huge data genreated by modern medical imaging device challenge the real time processing and rendering algorithms at all the time. Spurring by the great achievement of Points Based Rendering (PBR) in the fields of computer graphics to render very large meshes, we propose a new algorithm to use the points as basic primitive of surface reconstruction and rendering to interactively reconstruct and render very large volume dataset. By utilizing the special characteristics of medical image datasets, we obtain a fast and efficient points-based reconstruction and rendering algorithm in common PC. The experimental results show taht this algorithm is feasible and efficient.