Image processing and algorithms for medical applications
Image degradation is a significant issue in medical imaging, as it may obscure the fine details of anatomical structures and organizations. This not only reduces the usability of the images thus increasing the time and monetary costs associated with diagnostic procedures, but also significantly impa...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| التنسيق: | Thesis-Master by Coursework |
| اللغة: | English |
| منشور في: |
Nanyang Technological University
2024
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/10356/175599 |
| الوسوم: |
إضافة وسم
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| المؤسسة: | Nanyang Technological University |
| اللغة: | English |
| الملخص: | Image degradation is a significant issue in medical imaging, as it may obscure the fine details of anatomical structures and organizations. This not only reduces the usability of the images thus increasing the time and monetary costs associated with diagnostic procedures, but also significantly impact the accuracy of subsequent image analysis and interpretation. Noise and blur are are the two most common types of degradation. This project evaluates and compares the effectiveness of several image filtering methods, including Gaussian filter, Median filter, DWT filter, NLM filter, BM3D method, U-Net and Restormer model in restoring medical images with various levels and types of synthetic noise or blur to highlight the performance of deep learning based methods like U-Net and their great potential for application in medical image processing.
Keywords: Medical image processing, Medical image denoising, Medical image deblurring, Medical image filtering, Deep learning, U-Net |
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