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. S., Roohani Ghehsareh, H., & Etesami, S. K. (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
MLA
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
HARVARD
Seidzadeh, M. S., Roohani Ghehsareh, H., Etesami, S. K. (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
VANCOUVER
Seidzadeh, M. S., Roohani Ghehsareh, H., Etesami, S. K. 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
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