Biomedical Image Denoising Based on Hybrid Optimization Algorithm and Sequential Filters

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.

Simulation and Experimental Studies of Optimization of σ-Value for Block Matching and 3D Filtering Algorithm in Magnetic Resonance Images

“…In addition, the structural similarity index measurement (SSIM) and root mean square error (RMSE), which are similarity evaluation factors, were measured to evaluate how closely the degraded image was reconstructed from the noise-free reference [32,33]. The characteristic of simulation studies lies in the ability to obtain an ideal reference image with zero noise.…”

Generating pseudo‐computerized tomography (P‐CT) scan images from magnetic resonance imaging (MRI) images using machine learning algorithms based on fuzzy theory for radiotherapy treatment planning

“…In the noise removal method using optimization algorithms, objective functions such as peak signal‐to‐noise ratio (PSNR) and mean square error (MSE) can be defined, and the noise removal operation can be performed. Optimization algorithms can perform the best possible solution to select the best filtering parameters or even select the best sequence filters to eliminate noise 30 . The training of the regression model on aligned data is very important, as every single voxel in MRI should exactly correspond with the same voxel in CT. After registration, MRI images have the same size as CT and each pixel of MRI and CT have become correlated.…”

CT image denoising methods for image quality improvement and radiation dose reduction

“…Wavelet based denoising 5 11,[16][17][18]50 Threshold estimation 2 10,51 Shrinkage rules 2 17,19 Intra and inter scale dependencies based denoising 2 52,53 Image denoising based on extended versions of transform 6 14,21,51,[54][55][56] Block-matching and 3D filtering (BM3D) 4 15,25,57,58 image patches from a set of training images and uses this dictionary to denoise new images. This method can effectively reduce noise while preserving image details and structures.…”