Biomedical subjects

Huan-qing Feng

Publications and source records attributed to Huan-qing Feng.

3 recordsLinked to original sources

PubMed2003

Volume rendering based on sorted volumetric data.

OBJECTIVE: To describe a spatial data structure called sorted volumetric data structure which speeds up the volume rendering and will not affect image quality. METHOD: 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. RESULT: 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. CONCLUSION: 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.

Algorithms↗

PubMed2003

[Decomposition of EMG signals based on combination of information diffusion theory and fuzzy neural network].

OBJECTIVE: To solve the problem of large samples and contradictory samples in EMG during high level muscle contraction. METHOD: By means of recording EMG during muscle contraction with linearly increasing force instead of constant force, basic MUAP templates were obtained with the combination of information diffusion theory and fuzzy neural network. Samples were compressed and contradictory samples were eliminated. RESULT: The method was tested by simulated and real EMG data and the results were satisfactory. CONCLUSION: This method is meaningful for decomposing NEMG at high level muscle contraction.

Computer Simulation↗

PubMed2002

[Fast 3D surface rendering for CT or MR image on a personal computer].

Objective. To design a 3D surface reconstruction algorithm capable of reconstructing 3D shapes of the organs, bones and tissues rapidly from CT or MR image on a personal computer. Method. By transforming and decomposing the projection of the discrete 3D data field into shear transformation and warp transformation, a Shear-Warp algorithm realizing the 3D reconstruction was established. The algorithm reduce the amount of computation obviously. The 3D surface was reconstructed by using the principle of the parallel projection, basing on the Shear-Warp algorithm. Shear transformation was performed on the volume data at first. Then the warped 3D surface was obtained by making a threshold in the sheared space. The brightness of the 3D surface was calculated using Phong brightness model, and the final image was generated through the Warp transformation. Result. A direct 3D surface reconstruction algorithm was established basing on a principle of projection from the Shear-Warp algorithm, which was able to display 3D surface on a personal computer rapidly. Conclusion. Compared with other surface reconstruction algorithms, the proposed algorithm has the advantages of no preprocessing, no intermediate data are generated in the reconstruction. With this new algorithm, it is possible to realize fast 3D surface rendering for medical images on a personal computer without the support of any hardware accelerator.

Algorithms↗