{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:11:21Z","timestamp":1760195481414,"version":"build-2065373602"},"reference-count":12,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,2,5]],"date-time":"2018-02-05T00:00:00Z","timestamp":1517788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/jimaging4020034","type":"journal-article","created":{"date-parts":[[2018,2,5]],"date-time":"2018-02-05T06:23:04Z","timestamp":1517811784000},"page":"34","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Denoising of X-ray Images Using the Adaptive Algorithm Based on the LPA-RICI Algorithm"],"prefix":"10.3390","volume":"4","author":[{"given":"Ivica","family":"Mandi\u0107","sequence":"first","affiliation":[{"name":"Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hajdi","family":"Pei\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4058-8449","authenticated-orcid":false,"given":"Jonatan","family":"Lerga","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4758-7972","authenticated-orcid":false,"given":"Ivan","family":"\u0160tajduhar","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,2,5]]},"reference":[{"key":"ref_1","unstructured":"Buades, A., Coll, B., and Morel, J.M. (2018, February 04). On Image Denoising Methods. Available online: http:\/\/www3.cs.stonybrook.edu\/~cse577\/image.smoothing.morel.pdf."},{"key":"ref_2","first-page":"13","article-title":"A proposed approach for biomedical image denoising using PCA-NLM","volume":"6","author":"Bansal","year":"2014","journal-title":"Int. J. Bio-Sci. Bio-Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.21917\/ijivp.2015.0166","article-title":"M2 filter for speckle noise suppression in breast ultrasound images","volume":"6","author":"Samundeeswari","year":"2015","journal-title":"ICTACT J. Image Video Process."},{"key":"ref_4","unstructured":"Vijay, M., Suhha, S.V., and Karthik, K. (2002, January 21\u201322). Adaptive spatial and wavelet multiscale products thresholding method for medical image denoising. Proceedings of the International Conference on Computing, Electronics and Electrical Technologies, Kumaracoil, India."},{"key":"ref_5","first-page":"2","article-title":"MRI medical image denoising by fundamental filters","volume":"12","author":"Ali","year":"2017","journal-title":"SCIREA J. Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1469","DOI":"10.1109\/TIP.2005.851705","article-title":"A spatially adaptive nonparametric regression image deblurring","volume":"14","author":"Katkovnik","year":"2005","journal-title":"IEEE Trans. Image Process."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1023\/A:1020329726980","article-title":"Adaptive window size image de-noising based on intersection of confidence intervals (ICI) rule","volume":"16","author":"Katkovnik","year":"2002","journal-title":"J. Math. Imaging Vis."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11760-016-0921-6","article-title":"Improved LPA-ICI-based estimators embedded in a signal denoising virtual instrument","volume":"11","author":"Segon","year":"2017","journal-title":"Signal Image Video Process."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Katkovnik, V., Egiazarian, K., and Astola, J. (2006). Local Approximation Techniques in Signal and Image Processing, SPIE Publications\u2014The International Society for Optical Engineering.","DOI":"10.1117\/3.660178"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1109\/LSP.2008.2001817","article-title":"A signal denoising method based on the improved ICI rule","volume":"15","author":"Lerga","year":"2008","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_11","first-page":"33","article-title":"An adaptive method for video denoising based on the ICI rule","volume":"32","author":"Lerga","year":"2012","journal-title":"Eng. Rev."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/0167-2789(92)90242-F","article-title":"Nonlinear total variation based noise removal algorithms","volume":"60","author":"Rudin","year":"1992","journal-title":"Phys. D Nonlinear Phenom."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/4\/2\/34\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:53:49Z","timestamp":1760194429000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/4\/2\/34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,2,5]]},"references-count":12,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2018,2]]}},"alternative-id":["jimaging4020034"],"URL":"https:\/\/doi.org\/10.3390\/jimaging4020034","relation":{},"ISSN":["2313-433X"],"issn-type":[{"type":"electronic","value":"2313-433X"}],"subject":[],"published":{"date-parts":[[2018,2,5]]}}}