Denoising of X-ray Images Using the Adaptive Algorithm Based on the LPA-RICI Algorithm
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...
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doaj-a66608f0d5b84ea0bac6b99f3490c3eb2020-11-25T02:28:57ZengMDPI AGJournal of Imaging2313-433X2018-02-01423410.3390/jimaging4020034jimaging4020034Denoising of X-ray Images Using the Adaptive Algorithm Based on the LPA-RICI AlgorithmIvica Mandić0Hajdi Peić1Jonatan Lerga2Ivan Štajduhar3Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, CroatiaFaculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, CroatiaFaculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, CroatiaFaculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, CroatiaDiagnostics 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.http://www.mdpi.com/2313-433X/4/2/34adaptive filteringrelative intersection of confidence interval (RICI) algorithmimage denoisingmedical imaging |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ivica Mandić Hajdi Peić Jonatan Lerga Ivan Štajduhar |
spellingShingle |
Ivica Mandić Hajdi Peić Jonatan Lerga Ivan Štajduhar Denoising of X-ray Images Using the Adaptive Algorithm Based on the LPA-RICI Algorithm Journal of Imaging adaptive filtering relative intersection of confidence interval (RICI) algorithm image denoising medical imaging |
author_facet |
Ivica Mandić Hajdi Peić Jonatan Lerga Ivan Štajduhar |
author_sort |
Ivica Mandić |
title |
Denoising of X-ray Images Using the Adaptive Algorithm Based on the LPA-RICI Algorithm |
title_short |
Denoising of X-ray Images Using the Adaptive Algorithm Based on the LPA-RICI Algorithm |
title_full |
Denoising of X-ray Images Using the Adaptive Algorithm Based on the LPA-RICI Algorithm |
title_fullStr |
Denoising of X-ray Images Using the Adaptive Algorithm Based on the LPA-RICI Algorithm |
title_full_unstemmed |
Denoising of X-ray Images Using the Adaptive Algorithm Based on the LPA-RICI Algorithm |
title_sort |
denoising of x-ray images using the adaptive algorithm based on the lpa-rici algorithm |
publisher |
MDPI AG |
series |
Journal of Imaging |
issn |
2313-433X |
publishDate |
2018-02-01 |
description |
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. |
topic |
adaptive filtering relative intersection of confidence interval (RICI) algorithm image denoising medical imaging |
url |
http://www.mdpi.com/2313-433X/4/2/34 |
work_keys_str_mv |
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