Objective To accurately extract pulmonary vessels on medical images. Methods An efficient vessel segmentation framework is presented, which includes a smoothing method and a extraction algorithm. The smoothing method ...Objective To accurately extract pulmonary vessels on medical images. Methods An efficient vessel segmentation framework is presented, which includes a smoothing method and a extraction algorithm. The smoothing method is based on an improved coherence diffusion approach that integrates the second-order directional differential information. It can analyze weak edges such as narrow peak or ridge-like structures. Meanwhile, an improved extraction algorithm is proposed. It is based on a fast marching algorithm where a sorted sequence array and multi-initialization technique are applied. Results The improved coherence diffusion approach can precisely preserve important oriented patterns and remove noises on the images. Experimental results on several images show that the proposed method can effectively find the location of pulmonary vessels. Conclusion The segmentation method is accurate and fast that can be a useful tool for medical imaging applications.展开更多
Noise reduction is one of the most important concerns in electronic speckle pattern interferometry(ESPI). According to partial differential equation(PDE) filtering theory, we present an anisotropic PDE noisereduction ...Noise reduction is one of the most important concerns in electronic speckle pattern interferometry(ESPI). According to partial differential equation(PDE) filtering theory, we present an anisotropic PDE noisereduction model based on fringe structure information for interferometric fringe patterns. This model is based on coherence diffusion and Perona-Malik(P-M) diffusion. The former can protect the structure information of fringe pattern, while the latter can effectively filter off the noise inside the fringes. The proposed model generated by the two diffusion methods helps to obtain good effects of denoising and fidelity. ESPI fringes and the phase pattern are tested. Experimental results validate the performance of the proposed filtering model.展开更多
文摘Objective To accurately extract pulmonary vessels on medical images. Methods An efficient vessel segmentation framework is presented, which includes a smoothing method and a extraction algorithm. The smoothing method is based on an improved coherence diffusion approach that integrates the second-order directional differential information. It can analyze weak edges such as narrow peak or ridge-like structures. Meanwhile, an improved extraction algorithm is proposed. It is based on a fast marching algorithm where a sorted sequence array and multi-initialization technique are applied. Results The improved coherence diffusion approach can precisely preserve important oriented patterns and remove noises on the images. Experimental results on several images show that the proposed method can effectively find the location of pulmonary vessels. Conclusion The segmentation method is accurate and fast that can be a useful tool for medical imaging applications.
基金supported by the National Natural Science Foundation of China under Grant No.61102150
文摘Noise reduction is one of the most important concerns in electronic speckle pattern interferometry(ESPI). According to partial differential equation(PDE) filtering theory, we present an anisotropic PDE noisereduction model based on fringe structure information for interferometric fringe patterns. This model is based on coherence diffusion and Perona-Malik(P-M) diffusion. The former can protect the structure information of fringe pattern, while the latter can effectively filter off the noise inside the fringes. The proposed model generated by the two diffusion methods helps to obtain good effects of denoising and fidelity. ESPI fringes and the phase pattern are tested. Experimental results validate the performance of the proposed filtering model.
