Comparison of an adaptive local thresholding method on CBCT and $μ$CT endodontic images. Michetti, J., Basarab, A., Diemer, F., & Kouamé, D. Physics in Medicine and Biology, 63:1–10, IOP Science, http://iopscience.iop.org, January, 2018. ![Comparison of an adaptive local thresholding method on CBCT and $μ$CT endodontic images [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex Root canal segmentation on cone beam computed tomography (CBCT) images is difficult because of the noise level, resolution limitations, beam hardening and dental morphological variations. An image processing framework, based on an adaptive local threshold method, was evaluated on CBCT images acquired on extracted teeth. A comparison with high quality segmented endodontic images on micro computed tomography ($μ$CT) images acquired from the same teeth was carried out using a dedicated registration process. Each segmented tooth was evaluated according to volume and root canal sections through the area and the Feret's diameter. The proposed method is shown to overcome the limitations of CBCT and to provide an automated and adaptive complete endodontic segmentation. Despite a slight underestimation (-4, 08%), the local threshold segmentation method based on edge-detection was shown to be fast and accurate. Strong correlations between CBCT and $μ$CT segmentations were found both for the root canal area and diameter (respectively 0.98 and 0.88). Our findings suggest that combining CBCT imaging with this image processing framework may benefit experimental endodontology, teaching and could represent a first development step towards the clinical use of endodontic CBCT segmentation during pulp cavity treatment.
@Article{ MiBaDiKo2018.1,
author = {Michetti, J\'er�me and Basarab, Adrian and Diemer, Franck and Kouam\'e, Denis},
title = "{Comparison of an adaptive local thresholding method on CBCT and $\mu$CT endodontic images}",
journal = {Physics in Medicine and Biology},
publisher = {IOP Science},
address = {http://iopscience.iop.org},
year = {2018},
month = {January},
volume = {63},
pages = {1--10},
language = {anglais},
URL = {https://doi.org/10.1088/1361-6560/aa90ff - https://oatao.univ-toulouse.fr/22111/},
abstract = {Root canal segmentation on cone beam computed tomography (CBCT) images is difficult because of the noise level, resolution limitations, beam hardening and dental morphological variations. An image processing framework,
based on an adaptive local threshold method, was evaluated on CBCT images acquired on extracted teeth. A comparison with high quality segmented endodontic images on micro computed tomography ($\mu$CT) images acquired from the same
teeth was carried out using a dedicated registration process. Each segmented tooth was evaluated according to volume and root canal sections through the area and the Feret's diameter. The proposed method is shown to overcome the
limitations of CBCT and to provide an automated and adaptive complete endodontic segmentation. Despite a slight underestimation (-4, 08%), the local threshold segmentation method based on edge-detection was shown to be fast and
accurate. Strong correlations between CBCT and $\mu$CT segmentations were found both for the root canal area and diameter (respectively 0.98 and 0.88). Our findings suggest that combining CBCT imaging with this image processing
framework may benefit experimental endodontology, teaching and could represent a first development step towards the clinical use of endodontic CBCT segmentation during pulp cavity treatment.}
}
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An image processing framework, based on an adaptive local threshold method, was evaluated on CBCT images acquired on extracted teeth. A comparison with high quality segmented endodontic images on micro computed tomography ($μ$CT) images acquired from the same teeth was carried out using a dedicated registration process. Each segmented tooth was evaluated according to volume and root canal sections through the area and the Feret's diameter. The proposed method is shown to overcome the limitations of CBCT and to provide an automated and adaptive complete endodontic segmentation. Despite a slight underestimation (-4, 08%), the local threshold segmentation method based on edge-detection was shown to be fast and accurate. Strong correlations between CBCT and $μ$CT segmentations were found both for the root canal area and diameter (respectively 0.98 and 0.88). Our findings suggest that combining CBCT imaging with this image processing framework may benefit experimental endodontology, teaching and could represent a first development step towards the clinical use of endodontic CBCT segmentation during pulp cavity treatment.","bibtex":"@Article{ MiBaDiKo2018.1,\nauthor = {Michetti, J\\'er�me and Basarab, Adrian and Diemer, Franck and Kouam\\'e, Denis},\ntitle = \"{Comparison of an adaptive local thresholding method on CBCT and $\\mu$CT endodontic images}\",\njournal = {Physics in Medicine and Biology},\npublisher = {IOP Science},\naddress = {http://iopscience.iop.org},\nyear = {2018},\nmonth = {January},\nvolume = {63},\npages = {1--10},\nlanguage = {anglais},\nURL = {https://doi.org/10.1088/1361-6560/aa90ff - https://oatao.univ-toulouse.fr/22111/},\nabstract = {Root canal segmentation on cone beam computed tomography (CBCT) images is difficult because of the noise level, resolution limitations, beam hardening and dental morphological variations. An image processing framework,\nbased on an adaptive local threshold method, was evaluated on CBCT images acquired on extracted teeth. A comparison with high quality segmented endodontic images on micro computed tomography ($\\mu$CT) images acquired from the same\nteeth was carried out using a dedicated registration process. Each segmented tooth was evaluated according to volume and root canal sections through the area and the Feret's diameter. The proposed method is shown to overcome the\nlimitations of CBCT and to provide an automated and adaptive complete endodontic segmentation. Despite a slight underestimation (-4, 08%), the local threshold segmentation method based on edge-detection was shown to be fast and\naccurate. Strong correlations between CBCT and $\\mu$CT segmentations were found both for the root canal area and diameter (respectively 0.98 and 0.88). Our findings suggest that combining CBCT imaging with this image processing\nframework may benefit experimental endodontology, teaching and could represent a first development step towards the clinical use of endodontic CBCT segmentation during pulp cavity treatment.}\n}\n","author_short":["Michetti, J.","Basarab, A.","Diemer, F.","Kouamé, D."],"key":"MiBaDiKo2018.1","id":"MiBaDiKo2018.1","bibbaseid":"michetti-basarab-diemer-kouam-comparisonofanadaptivelocalthresholdingmethodoncbctandctendodonticimages-2018","role":"author","urls":{"Paper":"https://doi.org/10.1088/1361-6560/aa90ff - https://oatao.univ-toulouse.fr/22111/"},"metadata":{"authorlinks":{"kouamé, d":"https://bibbase.org/show?bib=https%3A%2F%2Fwww.irit.fr%2F%7EDenis.Kouame%2Fwp-content%2Fuploads%2Fsites%2F16%2F2023%2F12%2Fpubli_dk_Decembre2023.bib&commas=true&msg=embed&noBootstrap=1","kouam�, d":"https://bibbase.org/show?bib=https%3A%2F%2Fwww.irit.fr%2F%7EDenis.Kouame%2Fwp-content%2Fuploads%2Fsites%2F16%2F2020%2F08%2Fpubli_dk_pc.bib&msg=embed","basarab, a":"https://www.creatis.insa-lyon.fr/~basarab/publicationsBibBase.html"}}},"bibtype":"article","biburl":"https://bibbase.org/network/files/bvJMWi3fKm93LyCPk","creationDate":"2020-05-26T13:10:16.956Z","downloads":0,"keywords":[],"search_terms":["comparison","adaptive","local","thresholding","method","cbct","endodontic","images","michetti","basarab","diemer","kouamé"],"title":"Comparison of an adaptive local thresholding method on CBCT and $μ$CT endodontic images","year":2018,"dataSources":["KpLMFeykvtnr5Su9N","Ryxae6RgTenGcjg3E","dPx2jPYg3kJAFT4SQ","pYMoyBBvqRwgp2CAQ","a77xeD9CPH6zTuwds","ZP9Cm4xcHLMPzRbsw","YZr2vNEbZ3XwnTzeg","viHdZ5kJkWmsadWxH","3kvrPfJzi8tTnkCKu","BjuDhWDuYGoZ5Yxhc","L6xxxS29ikeRQS5yM","qj8D6CkroqAiZww2p","ammkpiTjv93R88jNJ","xjsywQmEcoyeGgrGH","J5cbtYq6pQvAHmgjz"]}