Abstract
A new approach of weighted gradient filter for denoising of medical images in the presence of Poisson noise
Highlights
In the medical field, digital images play a very significant role in analysis of the anatomy of various organs and are used in the recognition of various diseases
Luisier [21] proposed a Poisson denoising algorithm PURE-LET based on an unnormalized Haar wavelet transform and the minimization of an unbiased estimate of the Mean Square Error (MSE) for Poisson noise called "Poisson’s Unbiased Risk Estimate" (PURE)
Weighted Gradient filter method is applied on several case-study images of X-Rays, LENA & Peppers corrupted with Poisson noise
Summary
Digital images play a very significant role in analysis of the anatomy of various organs and are used in the recognition of various diseases. Errors during image acquisition process introduce noisy pixel values of image which do not reflect the real scene. Noise contamination in medical images results from different sources, for example, film grains introduce noise if image is acquired from a photograph film. Image de-noising removes additive noise and sustains the important signal features [2]. Different noises which may occur in medical images are Gaussian noise, Poisson noise, Speckle noise, Rician noise and Salt and pepper noise etc. MRI images are modelled with Rician Noise while Speckle and Poisson noises are observed in ultrasound and in x-rays respectively [3]. Disadvantage of Linear technique is that it damages the original pixels because the technique is applied on both noisy and original pixels. Nonlinear denoising consists of two steps, noise detection and noise substitution [5]. Noise location is detected in first step while detected noisy value is replaced by estimated value in second step
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
