Medical ultrasound images are usually degraded by a specific type of noise, called ”speckle”. The presence of speckle noise in medical ultrasound images will reduce the image quality and affect the effective information, which can potentially cause a misdiagnosis. Therefore, medical image enhancement processing has been extensively studied and several denoising approaches have been introduced and developed. In the current work, a robust fractional partial differential equation (FPDE) model based on the anomalous diffusion theory is proposed and used for medical ultrasound image enhancement. An efficient computational approach based on a combination of a time integration scheme and localized meshless method in a domain decomposition framework is performed to deal with the model. In order to evaluate the performance of the proposed de-speckling approach, it is used for speckle noise reduction of a synthetic ultrasound image degraded by different levels of speckle noise. The results indicate the superiority of the proposed approach in comparison with classical anisotropic diffusion denoising model (Catte’s pde model).
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Seidzadeh, M., Roohani Ghehsareh, H., Etesami, S. (2022). An anomalous diffusion approach for speckle noise reduction in medical ultrasound images. Computational Methods for Differential Equations, 10(1), 225-235. doi: 10.22034/cmde.2020.41858.1812
Maryam Sadat Seidzadeh; Hadi Roohani Ghehsareh; Seyed Kamal Etesami. "An anomalous diffusion approach for speckle noise reduction in medical ultrasound images". Computational Methods for Differential Equations, 10, 1, 2022, 225-235. doi: 10.22034/cmde.2020.41858.1812
Seidzadeh, M., Roohani Ghehsareh, H., Etesami, S. (2022). 'An anomalous diffusion approach for speckle noise reduction in medical ultrasound images', Computational Methods for Differential Equations, 10(1), pp. 225-235. doi: 10.22034/cmde.2020.41858.1812
Seidzadeh, M., Roohani Ghehsareh, H., Etesami, S. An anomalous diffusion approach for speckle noise reduction in medical ultrasound images. Computational Methods for Differential Equations, 2022; 10(1): 225-235. doi: 10.22034/cmde.2020.41858.1812