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In the light of current research and based on available publications, where such analysis is made on the basis of images obtained from standard radiography (x-ray), this is a new approach which uses microtomographic images (<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\mu $$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mi>\u03bc<\/mml:mi><\/mml:math><\/jats:alternatives><\/jats:inline-formula>-CT). In addition, the above-mentioned solutions most often analyze a pre-separated portion of an image, which requires the initial operator interference. The authors\u2019 own pre-processing methods, which allow to separate the element area and potential defect areas from <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\mu $$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mi>\u03bc<\/mml:mi><\/mml:math><\/jats:alternatives><\/jats:inline-formula>-CT images, and methods of extraction of selected features describing these areas have been proposed in the solution discussed here. A neural network trained using the Levenberg\u2013Marquardt method with error backpropagation has been used as a classifier. The optimal network structure 20\u20134\u20131 and a set of 20 features describing the analysed areas have been determined as a result of performed tests. The applied solutions have provided 89% correct detection for any defect size and 96.73% for large defects, which is comparable to the results obtained from methods using x-ray images. This has confirmed that it is possible to use <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\mu $$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mi>\u03bc<\/mml:mi><\/mml:math><\/jats:alternatives><\/jats:inline-formula>-CT images in automatic defect localization in 3D. Thanks to this method, quantitative analysis of aluminium castings can be carried out without user interaction and fully automated.\n<\/jats:p>","DOI":"10.1007\/s00138-020-01084-3","type":"journal-article","created":{"date-parts":[[2020,5,27]],"date-time":"2020-05-27T03:51:41Z","timestamp":1590551501000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Analysis of microtomographic images in automatic defect localization and detection"],"prefix":"10.1007","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6244-5588","authenticated-orcid":false,"given":"Mariusz","family":"Marzec","sequence":"first","affiliation":[]},{"given":"Piotr","family":"Duda","sequence":"additional","affiliation":[]},{"given":"Zygmunt","family":"Wr\u00f3bel","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,5,27]]},"reference":[{"issue":"1","key":"1084_CR1","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.nic.2017.09.006","volume":"28","author":"JN Useche","year":"2018","unstructured":"Useche, J.N., Bermudez, S.: Conventional computed tomography and magnetic resonance in brain concussion. 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