Medical Image Denoising Using Bilateral Filter

Abstract

Abstract-Medical image processing is used for the diagnosis of diseases by the physicians or radiologists. Noise is introduced to the medical images due to various factors in medical imaging. Noise corrupts the medical images and the quality of the images degrades. This degradation includes suppression of edges, structural details, blurring boundaries etc. To diagnose diseases edge and details preservation are very important. Medical image denoising can help the physicians to diagnose the diseases. Medical images include MRI, CT scan, x-ray images, ultrasound images etc. In this paper we implemented bilateral filtering for medical image denoising. Its formulation & implementation are easy but the performance of bilateral filter depends upon its parameter. Therefore for obtaining the optimum result parameter must be estimated. We have applied bilateral filtering on medical images which are corrupted by additive white Gaussian noise with different values of variances. It is a nonlinear and local technique that preserves the features while smoothing the images. It removes the additive white Gaussian noise effectively but its performance is poor in removing salt and pepper noise.

Watermarking of radiographic images based on quantum noise modulation

“…Many denoising methods have been proposed for radiographic images [14][15][16]. Some of them work in the spatial domain [14] and the others in transformed domains (e.g.…”

“…Some of them work in the spatial domain [14] and the others in transformed domains (e.g. wavelet [15], multi-wavelet decomposition [16]).…”

An Improved Method for Automatic Segmentation and Accurate Detection of Brain Tumor in Multimodal MRI

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Speckle Reduction in Ultrasound Images Using Neighshrink and Bilateral Filtering

“…The bilateral filter was proposed as an alternative to wavelet thresholding (Tomasi and Manduchi, 1998). Bhonsle et al (2012) used bilateral filter for denoising of medical images and the filter performed well in the case of Additive White Gaussian Noise (AWGN) compared to speckle noise. To improve the efficiency of wavelet thresholding techniques, efforts have been taken to hybridize with spatial domain filters.…”