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For researchers, denoising of Magnetic Resonance (MR) image is a greatest challenge in digital image processing. In this paper, the impulse noise and Rician noise in the medical MR images are removed by using Bilateral Filter (BF). The novel approaches are presented in this paper; Enhanced grasshopper optimization algorithm (EGOA) is used to optimize the BF parameters. To simulate the medical MR images (with different variances), the impulse and Rician noises are added. The EGOA is applied to the noisy image in searching regions of window size, spatial and intensity domain to obtain the filter parameters optimally. The PSNR is taken as fitness value for optimization. We examined the proposed technique results with other MR images After the optimal parameters assurance. In order to comprehend the BF parameters selection importance, the results of proposed denoising method is contrasted with other previously used BFs, genetic algorithm (GA), gravitational search algorithm (GSA) using the quality metrics such as signal-to-noise ratio (SNR), structural similarity index metric (SSIM), mean squared error (MSE), and PSNR. The outcome shows that the EOGA method with BF shows good results than the earlier methods in both edge preservation and noise elimination from medical MR images. The experimental results demonstrate the performance of the proposed method with the accuracy, computational time, and maximum deviation, Peak Signal to Noise Ratio (PSNR), MSE, SSIM, and entropy values of MR images over the existing methods. [ABSTRACT FROM AUTHOR]

Typically, the acquired renal ultrasound image includes a course of speckle noises. This paper primarily investigates an approach for the detection of renal calculi by processing those raw US images with the help of a meta-heuristic SVM classifier. One of the major downsides of involving Ultrasound images in medical analysis is the prevalence of Speckle Noises. An Adaptive Mean Median Filter approach has been introduced in the work to get rid of the speckle noises to the maximum extent ever in the literature. Segmentation is performed by employing conventional K-Means and GLCM features were extracted for classification using a meta-heuristic SVM classifier. The proposed methodology investigates with a Real-time Acquired Dataset of Mithra Scans, Tamilnadu, India comprises of 250 clinical Ultra-Sound Kidney Images of which 150 are having Calculi and the rest are Healthy. With the experimental results, the proposed meta-heuristic SVM classifier have performed better in noisy images while comparing with other conventional methods considered in the literature. It exhibits an Accuracy of 98.8% with a FAR rate of 1.8 for FRR as high as 3.3. The results clearly proposed that the novel AMM-PSO-SVM could be a promising technique for object or foreign body detection in a medical imaging application that uses ultrasound imaging. [ABSTRACT FROM AUTHOR]

The one of the preprocessing step for hyperspectral imagery is noise reduction. The images are received by the detector and this can be degraded by several factors like atmospherical things and device noises which emit temperature noise, processing noise and explosion noise. There are several strategies are developed already to cut back the signal to noise magnitude relation of the hyperspectral image. However, the stationary noise of the many denoising ways developed cannot be applied on to the gauge boson noise. Thus, the each gauge boson and thermal noise square measure gift within the captured hyperspectral image (HSI). during this paper, we tend to projected a replacement denoising framework known as tensor-based filtering employing a PARAFAC tensor decomposition methodology for scale back each noise. The proposed technique is performs higher in removing noise as compared with different strategies like Multiple linear regression (MLR) algorithm and combined algorithm called multidimensional wavelet transforms with multiway wiener filter (MWPT-MWF) technique. The performance analysis of the new denoising framework has more efficient for reducing signal dependent (PN) and signal independent noise (TN) as compared with other conventional method. Hence this novel denoising approach would be more beneficial for detection of skin allergy and also this algorithm will be very useful for detection of retinal exudates and diagnosis of diabetes mellitus and retinopathy disease in medical application. [ABSTRACT FROM AUTHOR]

The quality of Magnetic Resonance Images(MRI) are degraded by the various types of noises. In this paper, a Hybrid Multi-resolution filter for denoising the MRI images degraded by the Salt and Pepper noise is proposed and the wavelet transform is used to improve the resolution of the denoised image.. The Hybrid filter consist of three value weighted filter and similarity based filter. In three value weighted filter, a variable local window is applied to find the noisy pixels. By using the noise free pixels in that window, the noisy pixels are reconstructed using three value method. In similarity based filter, a variable local window is applied to reconstruct the noisy pixels. In that window, based on the similarity between the noisy pixel sequence and noise free pixels sequence are used to reconstruct the noisy pixel. At last wavelet transform is used to increase the resolution of the reconstructed image. The experimental results shows that the proposed filter denoises the image and improves the resolution when compared to the existing methods and produces the efficiency of about 98%. [ABSTRACT FROM AUTHOR]