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Medical image denoising using low pass filtering in sparse domain.
Kaveh Abhari1, Mahdi Marsousi, Paul Babyn
1Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, M5B 2K3, Canada. kaveh.abhari@ryerson.ca
Summary
This study introduces a novel medical image denoising method using sparse coding and a new Discrete Cosine Transform dictionary. The approach effectively reduces noise and blocking artifacts in low-dose CT images, improving image quality.
Area of Science:
- Medical Imaging
- Image Processing
- Computational Science
Background:
- Medical images often suffer from noise, particularly in low-dose protocols like CT scans.
- Existing denoising methods may introduce artifacts or fail to adequately suppress noise while preserving image details.
Purpose of the Study:
- To develop an innovative medical image denoising technique.
- To enhance the quality of low-dose Computed Tomography (CT) images by reducing noise and artifacts.
Main Methods:
- Extending low-pass filtering concepts into a sparse representation framework.
- Applying a weight matrix to sparse coding optimization to suppress high-frequency noise components.
- Constructing an overcomplete Discrete Cosine Transform (DCT) dictionary incorporating frequency and phase information to mitigate blocking artifacts.
Main Results:
- The proposed method demonstrated qualitative and quantitative improvements on low-dose CT phantoms.
- Significant enhancements in peak signal to noise ratio (PSNR) were observed compared to previous denoising approaches.
Conclusions:
- The novel sparse representation and DCT-based approach offers superior performance for medical image denoising.
- This method effectively reduces noise and blocking artifacts, leading to better image quality in low-dose CT scans.
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