An Improved Denoised 3D Edge Extraction Operator within Biomedical Images

The previous 3D edge surface detector based on the Laplace and gradient operator can extract high accuracy edge surfaces with high efficiency in contrast with traditional isotropic surface extraction operator. However, the second derivat1ive in the 3D detector shows natural sensitivity to noise, which generates the noise polluted 3D edge surfaces and noisy pieces. A novel denoising 3D edge detector is proposed; the noisy image is filtered by the 3D Gauss filter firstly, then edge surfaces are detected and extracted utilizing the traditional 3D edge surface detector. Furthermore, the extracted 3D noisy edge surface pieces are degraded by the tracking technique. Finally, the denoising 3D edge surfaces are converted to polygon pieces, then visualized the surface with combined image and graphic methods. Experimental results show that the proposed scheme suppresses noise and preserves edge surfaces than the traditional 3D edge surface detector.