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An Anomalous Diffusion Approach for Speckle Noise Reduction in Medical Ultrasound Images | ||
| Computational Methods for Differential Equations | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 16 دی 1399 اصل مقاله (1023.71 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22034/cmde.2020.41858.1812 | ||
| نویسندگان | ||
Hadi Roohani Ghehsareh  1؛ Maryam Sadat Seidzadeh2؛ Seyed Kamal Etesami2
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| 1Department of Mathematics, Malek Ashtar University of Technology, Shahin Shahr, Isfahan, 83145-115, Iran | ||
| 2Department of Mathematics, Faculty of Applied Sciences, Malek Ashtar University of Technology, Shahin Shahr, Iran. | ||
| چکیده | ||
| 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 (Catt$\acute{e}$'s pde model).} | ||
| کلیدواژه‌ها | ||
| Medical Ultrasound Images؛ Images Denoising؛ Fractional Perona-Malik Equation؛ Localized Meshfree Method؛ Domain Decomposition Method | ||
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آمار تعداد مشاهده مقاله: 103 تعداد دریافت فایل اصل مقاله: 88 |
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