Abstract
Diagnostics and treatments of numerous diseases are highly dependent on the quality of captured medical images. However, noise (during both acquisition and transmission) is one of the main factors that reduce their quality. This paper proposes an adaptive image denoising algorithm applied to enhance X-ray images. The algorithm is based on the modification of the intersection of confidence intervals (ICI) rule, called relative intersection of confidence intervals (RICI) rule. For each image pixel apart, a 2D mask of adaptive size and shape is calculated and used in designing the 2D local polynomial approximation (LPA) filters for noise removal. One of the advantages of the proposed method is the fact that the estimation of the noise free pixel is performed independently for each image pixel and thus, the method is applicable for easy parallelization in order to improve its computational efficiency. The proposed method was compared to the Gaussian smoothing filters, total variation denoising and fixed size median filtering and was shown to outperform them both visually and in terms of the peak signal-to-noise ratio (PSNR) by up to 7.99 dB.
Highlights
Denoising is one of the most important fields in signal processing and the pursuit for novel and more efficient denoising methods is constant [1]
2015b and denoising performances were measured in terms of the implemented in the Matlab 2015b and denoising performances were measured in terms of the peak peak signal-to-noise ratio (PSNR)
The method was implemented in the Matlab 2015b and denoising performances were measured in terms of the peak signal-to-noise ratio (PSNR)
Summary
Denoising (both of one-dimensional and multi-dimensional signals) is one of the most important fields in signal processing and the pursuit for novel and more efficient denoising methods is constant [1]. Numerous methods developed over the last few decades found their applications in various fields, including medical image processing and analysis leading to enhancements in examining the interior of the human body without surgeries. Since both revealing and treatment of large number of diseases today is highly dependent on medical images (such as computed tomography (CT), ultrasound, magnetic resonance imaging (MRI), X-rays etc.), improving their quality is an essential precondition for their analysis. Noise has to be suppressed in order to decrease the probability of possible misinterpretations and incorrect diagnoses [2] Due to their cost and a decrease in the dose of ionizing radiation, X-rays are still the most frequently used medical imaging technique. Lower doses of radiation require more efficient noise reduction methods such that the important image features (object contours, edges, textures, etc.)
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