Visualization for Non-linear Enhanced Volume Data
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There is a strong need for acquisition of high quality volume data sets, with the vision of visualizing organs, engines or other materials for variant fields. Visualized computer model provides interactive investigation and improves understanding of details of substances. Since the quality of volume data which is acquired by CT and MRI devices is impacted by noisy, partial effect and bias seriously, in this paper we first apply non-linear filters in pre-processing stage to improve the quality of 3D volume data, followed by construction of 2D histogram based on external voxels to design the transfer function. Finally, we use ray-casting algorithm to render volume data. We applied the method presented above to four datasets. The noise is suppressed effectively after denoising with nonlinear anisotropic filtering. Boundary between materials can be following differentiated clearly by designing transfer function with 2D histogram constructed based on the pre-processed data.Keywords:
Ray casting
In order to realize real-time rendering for medical images on PCs,a scheme combining multi factors for assigning optical attribute to resampling points is defined,and then a novel rendering algorithm for medical volumetric data is presented.The volumetric data are classified into the foreground voxels and background voxels,and the foreground voxels are resampled using Layer of Detail(LOD) technique.Then,the optical attributes of resampling points are determined by the defined method,which is reasonably associated with the distances between object and viewpoint,as well as object and light source.Finally,the background voxels are displayed by the accelerated method based on space leaping resampling.The experimental results indicate the presented algorithm can reach up to 2.5 frame/s clearly on PCs for 512×512×482×2 Byte volumetric data,when it reduced to 2/3 times of the original size.It comes to the conclusion that the interactive volume rendering can be implemented for most medical volume data on PCs. Moreover,tissues or organs can be displayed clearly,which is more coincident with the human vision.
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To show the cerebrovascular volume date segmented from medical images,a new integration of multi-factor volume rendering algorithm was proposed.Firstly,the cerebral vascular volume data was classified into vascular and background voxels.Secondly,in the base of Ray-Casting,a new integration of multi-fusion volume rendering algorithm was proposed,considering cerebrovascular edge features,the distance between the viewpoint and the object,and the distance of the light source and the object.Finally,volume rendering was accelerated by using CUDA.The experimental results show that the cerebral vascular volume data can be displayed clearly,which is more coincident with the human vision,and real-time interaction.
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Ray casting algorithm is one important method to render CT volumes with 2D CT slices, and it can be accelerated using graphics hardware. In this paper, we developed an efficient volume rendering system based on ray casting using C++ and VTK, and designed common shading and classification transfer functions to highlight different organs/tissues of a CT volume. With this system we experimented CT volumes using CPU ray casting and GPU ray casting respectively, and then we did the same experiments on different graphics cards. The results showed that we can highlight the organs/tissues in which we are interested by setting proper transfer functions and that the rendering efficiency can be accelerated greatly using graphics cards with nVidia GPU GT220 or higher almost with the same visualization quality as that of CPU ray casting.
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To describe a spatial data structure called sorted volumetric data structure which speeds up the volume rendering and will not affect image quality.Before volume rendering we transfer each slice of volume into an array indexed by the value of voxels, element in which stores the position of each voxel. According to the opacity transfer function, we can affirm the value ranges of voxels that are non-transparent. Therefore we translate, resample and composite only the voxels in the value ranges. By marching through the sorted arrays, we locate the non-transparent voxels rapidly and skip all voxels that are transparent. The sorted data structures need not recompute whenever the opacity transfer function changes and are not restricted on the opacity transfer function.The method presented in this paper has been implemented in a standard personal computer. The rendering time for CT head volume data is less than 1 s that is satisfied with the diagnostic purpose.The concept of proposed algorithm is simple, easy to realize and it is not recomputed whenever the opacity transfer function changes. By using the sorted volumetric data structure, we speed up the volume rendering without affecting image quality.
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This paper discusses the principle and implementation method of Ray-casting volume rendering algorithm. In order to enhance the image quality and speed of alternate operation, we improve the grads formula in Ray-casting volume rendering algorithm and compound method of the sampling points.
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Volume visualization is one of the advanced technologies in the field of medical image 3D visualization, the efficiency of the volume rendering algorithm directly influences the results of volume visualization. In allusion to the ray-casting volume rendering algorithm, a fast look-up empty voxels algorithm is proposed based on AABB, which shortens the length of ray integral and accelerates the volume rendering. The experiment results verify that the new algorithm is effective in raising rendering speed.
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Ray casting algorithm is achieved through using of the tools of VC++6.0 and OpenGL.Introduced the ray casting algorithm and the knowledge of OpenGL.The crucial experimental method is following.Firstly,the regular volume data needed in the process of volume rendering are obtained by the preprocess of original CT images,and the values of color and opacity are distributed to every data.Secondly,the image of head which the main part is the brain of person is rendered with the volume rendering,and the function of browse and rendering can be completed in three directions of space.However,the speed of rendering is slow.Experimental results show that a proper example of volume rendering is exemplified in the visualization of 3D the person's brain by ray casting algorithm,and the image of rendering is quite clear,the result of image is very satisfied.
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In volume rendering, an important issue in acceleration is to reduce the calculations for occluded voxels. Although this issue has been addressed in the ray casting approach, it is difficult to apply the idea to the projection approach due to uncertain termination conditions. In this paper, we propose a new method to effectively address the exclusion problem in the projection approach, so the rendering process can be accelerated without impairing the rendered image quality. In the rendering process, this new method employs the dynamic screen technique to manage the pixels whose accumulated opacity has not reached 1.0. A ray-cast link at each pixel is set up to record the rendered voxels for the corresponding ray cast from the pixel to intersect. According to the rendered voxels covering the pixels whose accumulated opacity value is below 1.0, visible voxels are selected to render from front to back by the neighboring relationship between the rendered voxels and the voxels to be rendered. Thus, the occluded voxels are dynamically excluded from the loading and rendering processes accurately. Our proposed method can be in general applied to both parallel and perspective projections, using regular and irregular volume datasets. Our experimental results showed that the proposed method can significantly accelerate volume rendering if the data volume has a high percentage of occluded voxels. This method can also perform fairly efficiently for the expensive shading calculations if requested in volume rendering.
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Three dimensional visualization of medical volume data is achieved by many of the volume rendering algorithms either Direct or Indirect. The focus of our work is to make some simplifications in the Ray casting algorithm, a Direct Volume Rendering technique. The idea is to group the rays that are cast from similar voxels and the groups are verified for similarity on every successive sampling. This paper also compares the results with those results generated by the traditional ray casting in order ensure image quality. As the rays are cast in groups, the computational complexity of the rendering algorithm is reduced due to the decrease in the total number of samples being actually processed.
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Volume ray casting algorithm is widely recognized for high quality volume visualization. However, when rendering very large volume data sets, the original ray casting algorithm will lead to very inefficient random accesses in disk and make it very slowly to render the whole volume data set. In order to solve this problem, an efficient out-of-core volume ray casting method with a new out-of-core framework for processing large volume data sets based on consumer PC hardware is proposed in this paper. The new framework gives a transparent and efficient access to the volume data set cached in disk, while the new volume ray casting method minimizes the data exchange between hard disk and physical memory and performs comparatively fast high quality volume rendering. The experimental results indicate that the new method and framework are effective and efficient for the visualization of very large medical data sets.
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