Digital Signal Processing Reference
In-Depth Information
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Fig. 7.
The comparison between the tradition edge surfaces and the improved denoised detection
algorithm
All the experiments were carried on a PC with a Pentium IV 2.6 GHz CPU and 1G
byte dynamic RAM, using Visual C++ 6.0 and OpenGL. Although the new object is
slightly changed by the improved 3D edge surface detector, it contains important
features of the original object.
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Conclusions
The sub-voxel 3D edge surfaces extracted from noisy volume data is exploited in this
paper, via the method combination of the 3D Gauss filter and the traditional 3D edge
surfaces detector based on Laplace and Gradient method. The previous work of the
traditional 3D edge detector has not considered the affection of the noise [3], [4], [5], [6]
due to the natural sensitivity of Laplace operator. As a result, the noisy edge surfaces
appeared. The scheme proposed in this paper is an extension of previous work done by
Ma Yu and Lisheng Wang [3], [4], [5], [6]; the former is the author of this paper. Firstly,
the 3D Gauss filter denoises the noisy volume images, and then the sub-voxel edge
surfaces are extracted. Furthermore, the extracted 3D noisy patches are removed by the
tracking technique based on the coplanar principle within 3D cubes. Experimental
results show the validity and effectiveness of the improved 3D denoised edge detection
algorithm, which can overcome the shortcoming in the traditional 3D edge surface
detector and easily be extended to other application and analysis within 3D biomedical
and industrial images.
