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Biomedical Image Denoising Based on Hybrid Optimization Algorithm and Sequential Filters

Document Type : Original Research

Authors

1 PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran

2 MD, Department of Radiation Oncology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran

3 PhD, Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran

Abstract

Background: Nowadays, image de-noising plays a very important role in medical analysis applications and pre-processing step. Many filters were designed for image processing, assuming a specific noise distribution, so the images which are acquired by different medical imaging modalities must be out of the noise.
Objectives: This study has focused on the sequence filters which are selected by a hybrid genetic algorithm and particle swarm optimization.
Material and Methods: In this analytical study, we have applied the composite of different types of noise such as salt and pepper noise, speckle noise and Gaussian noise to images to make them noisy. The Median, Max and Min filters, Gaussian filter, Average filter, Unsharp filter, Wiener filter, Log filter and Sigma filter, are the nine filters that were used in this study for the denoising of medical images as digital imaging and communications in medicine (DICOM) format.
Results: The model has been implemented on medical noisy images and the performances have been determined by the statistical analyses such as peak signal to noise ratio (PSNR), Root Mean Square error (RMSE) and Structural similarity (SSIM) index. The PSNR values were obtained between 59 to 63 and 63 to 65 for MRI and CT images. Also, the RMSE values were obtained between 36 to 47 and 12 to 20 for MRI and CT images.
Conclusion: The proposed denoising algorithm showed the significantly increment of visual quality of the images and the statistical assessment.

Keywords

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Journal of Biomedical Physics and Engineering
Volume 10, Issue 1
37
January and February 2020
Pages 83-92
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