A robust head MRI/CT background removing approach using dynamic morphological operations
Objectives: The objective of the work is to remove the CT table or other noises from Head CT/MRI images, which are often present and effectively decrease the efficiency of further intended image processing operations. Methods: At first, depending on the mass an adaptive ellipse-shaped mask is genera...
Ausführliche Beschreibung
Autor*in: |
Halder, Tanmoy Kanti [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2023 |
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Anmerkung: |
© Indian National Science Academy 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Übergeordnetes Werk: |
Enthalten in: Proceedings of the Indian National Science Academy - Indian National Science Academy, 2022, 89(2023), 3 vom: 21. Juni, Seite 673-688 |
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Übergeordnetes Werk: |
volume:89 ; year:2023 ; number:3 ; day:21 ; month:06 ; pages:673-688 |
Links: |
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DOI / URN: |
10.1007/s43538-023-00175-9 |
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Katalog-ID: |
SPR052880206 |
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700 | 1 | |a Mandal, Ardhendu |4 aut | |
700 | 1 | |a Biswas, Saroj Kr |4 aut | |
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10.1007/s43538-023-00175-9 doi (DE-627)SPR052880206 (SPR)s43538-023-00175-9-e DE-627 ger DE-627 rakwb eng Halder, Tanmoy Kanti verfasserin aut A robust head MRI/CT background removing approach using dynamic morphological operations 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Indian National Science Academy 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Objectives: The objective of the work is to remove the CT table or other noises from Head CT/MRI images, which are often present and effectively decrease the efficiency of further intended image processing operations. Methods: At first, depending on the mass an adaptive ellipse-shaped mask is generated to segment out the Region of interest (ROI) area. Then an initial head mask has been generated using iterative morphology. Finally, head mask boundary coordinates are corrected accordingly to fully cover the head portion. Findings: The result section shows that the proposed method is more effective on normal brain MRI than skull stripped MRIs. The proposed method performs well in comparison with static morphology-based methods. For MRI with skull images, the score is 97.03% and for skull stripped MRI images the score is about 91%. In the case of brain CT, the proposed method achieved almost 90% Jaccard coefficient similarity index value. These results prove the efficiency and accuracy of the proposed method the results are compared with the existing well-known methods of Atkins et al. (IEEE Trans Med Imaging, 17(1), 98–107, https://doi.org/10.1109/42.668699, 1998), Liu et al. (EURASIP J Image Video Process, 2017(1), https://doi.org/10.1186/s13640-017-0209-y, 2017), Mol et al. (IOP Conf Ser Mater Sci Eng 396, 012039, https://doi.org/10.1088/1757-899x/396/1/012039, 2018), Chen et al. (Real-time patient table removal in CT images. Health Information Science, pp 1–8, Springer, https://doi.org/10.1007/978-3-319-48335-1_1, 2016) and Maureenvan Eijnatten et al. (Comput Methods Progr Biomed 208, 106261, https://doi.org/10.1016/j.cmpb.2021.106261, 2021). Novelty: The proposed method can remove any kind of background noises from both CT and MRI images. Here, the value of morphological operators is determined dynamically through iteration. Moreover, this work proposed a novel CT patient table removing approach from Brain CT images often are undesirable part of the medical image. CT background removing (dpeaa)DE-He213 Dynamic morphological operator (dpeaa)DE-He213 Head masking (dpeaa)DE-He213 MRI background removing (dpeaa)DE-He213 Sarkar, Kanishka (orcid)0000-0001-9695-894X aut Mandal, Ardhendu aut Biswas, Saroj Kr aut Enthalten in Proceedings of the Indian National Science Academy Indian National Science Academy, 2022 89(2023), 3 vom: 21. Juni, Seite 673-688 (DE-627)78783341X (DE-600)2773381-6 2454-9983 nnns volume:89 year:2023 number:3 day:21 month:06 pages:673-688 https://dx.doi.org/10.1007/s43538-023-00175-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 89 2023 3 21 06 673-688 |
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10.1007/s43538-023-00175-9 doi (DE-627)SPR052880206 (SPR)s43538-023-00175-9-e DE-627 ger DE-627 rakwb eng Halder, Tanmoy Kanti verfasserin aut A robust head MRI/CT background removing approach using dynamic morphological operations 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Indian National Science Academy 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Objectives: The objective of the work is to remove the CT table or other noises from Head CT/MRI images, which are often present and effectively decrease the efficiency of further intended image processing operations. Methods: At first, depending on the mass an adaptive ellipse-shaped mask is generated to segment out the Region of interest (ROI) area. Then an initial head mask has been generated using iterative morphology. Finally, head mask boundary coordinates are corrected accordingly to fully cover the head portion. Findings: The result section shows that the proposed method is more effective on normal brain MRI than skull stripped MRIs. The proposed method performs well in comparison with static morphology-based methods. For MRI with skull images, the score is 97.03% and for skull stripped MRI images the score is about 91%. In the case of brain CT, the proposed method achieved almost 90% Jaccard coefficient similarity index value. These results prove the efficiency and accuracy of the proposed method the results are compared with the existing well-known methods of Atkins et al. (IEEE Trans Med Imaging, 17(1), 98–107, https://doi.org/10.1109/42.668699, 1998), Liu et al. (EURASIP J Image Video Process, 2017(1), https://doi.org/10.1186/s13640-017-0209-y, 2017), Mol et al. (IOP Conf Ser Mater Sci Eng 396, 012039, https://doi.org/10.1088/1757-899x/396/1/012039, 2018), Chen et al. (Real-time patient table removal in CT images. Health Information Science, pp 1–8, Springer, https://doi.org/10.1007/978-3-319-48335-1_1, 2016) and Maureenvan Eijnatten et al. (Comput Methods Progr Biomed 208, 106261, https://doi.org/10.1016/j.cmpb.2021.106261, 2021). Novelty: The proposed method can remove any kind of background noises from both CT and MRI images. Here, the value of morphological operators is determined dynamically through iteration. Moreover, this work proposed a novel CT patient table removing approach from Brain CT images often are undesirable part of the medical image. CT background removing (dpeaa)DE-He213 Dynamic morphological operator (dpeaa)DE-He213 Head masking (dpeaa)DE-He213 MRI background removing (dpeaa)DE-He213 Sarkar, Kanishka (orcid)0000-0001-9695-894X aut Mandal, Ardhendu aut Biswas, Saroj Kr aut Enthalten in Proceedings of the Indian National Science Academy Indian National Science Academy, 2022 89(2023), 3 vom: 21. Juni, Seite 673-688 (DE-627)78783341X (DE-600)2773381-6 2454-9983 nnns volume:89 year:2023 number:3 day:21 month:06 pages:673-688 https://dx.doi.org/10.1007/s43538-023-00175-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 89 2023 3 21 06 673-688 |
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10.1007/s43538-023-00175-9 doi (DE-627)SPR052880206 (SPR)s43538-023-00175-9-e DE-627 ger DE-627 rakwb eng Halder, Tanmoy Kanti verfasserin aut A robust head MRI/CT background removing approach using dynamic morphological operations 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Indian National Science Academy 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Objectives: The objective of the work is to remove the CT table or other noises from Head CT/MRI images, which are often present and effectively decrease the efficiency of further intended image processing operations. Methods: At first, depending on the mass an adaptive ellipse-shaped mask is generated to segment out the Region of interest (ROI) area. Then an initial head mask has been generated using iterative morphology. Finally, head mask boundary coordinates are corrected accordingly to fully cover the head portion. Findings: The result section shows that the proposed method is more effective on normal brain MRI than skull stripped MRIs. The proposed method performs well in comparison with static morphology-based methods. For MRI with skull images, the score is 97.03% and for skull stripped MRI images the score is about 91%. In the case of brain CT, the proposed method achieved almost 90% Jaccard coefficient similarity index value. These results prove the efficiency and accuracy of the proposed method the results are compared with the existing well-known methods of Atkins et al. (IEEE Trans Med Imaging, 17(1), 98–107, https://doi.org/10.1109/42.668699, 1998), Liu et al. (EURASIP J Image Video Process, 2017(1), https://doi.org/10.1186/s13640-017-0209-y, 2017), Mol et al. (IOP Conf Ser Mater Sci Eng 396, 012039, https://doi.org/10.1088/1757-899x/396/1/012039, 2018), Chen et al. (Real-time patient table removal in CT images. Health Information Science, pp 1–8, Springer, https://doi.org/10.1007/978-3-319-48335-1_1, 2016) and Maureenvan Eijnatten et al. (Comput Methods Progr Biomed 208, 106261, https://doi.org/10.1016/j.cmpb.2021.106261, 2021). Novelty: The proposed method can remove any kind of background noises from both CT and MRI images. Here, the value of morphological operators is determined dynamically through iteration. Moreover, this work proposed a novel CT patient table removing approach from Brain CT images often are undesirable part of the medical image. CT background removing (dpeaa)DE-He213 Dynamic morphological operator (dpeaa)DE-He213 Head masking (dpeaa)DE-He213 MRI background removing (dpeaa)DE-He213 Sarkar, Kanishka (orcid)0000-0001-9695-894X aut Mandal, Ardhendu aut Biswas, Saroj Kr aut Enthalten in Proceedings of the Indian National Science Academy Indian National Science Academy, 2022 89(2023), 3 vom: 21. Juni, Seite 673-688 (DE-627)78783341X (DE-600)2773381-6 2454-9983 nnns volume:89 year:2023 number:3 day:21 month:06 pages:673-688 https://dx.doi.org/10.1007/s43538-023-00175-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 89 2023 3 21 06 673-688 |
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10.1007/s43538-023-00175-9 doi (DE-627)SPR052880206 (SPR)s43538-023-00175-9-e DE-627 ger DE-627 rakwb eng Halder, Tanmoy Kanti verfasserin aut A robust head MRI/CT background removing approach using dynamic morphological operations 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Indian National Science Academy 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Objectives: The objective of the work is to remove the CT table or other noises from Head CT/MRI images, which are often present and effectively decrease the efficiency of further intended image processing operations. Methods: At first, depending on the mass an adaptive ellipse-shaped mask is generated to segment out the Region of interest (ROI) area. Then an initial head mask has been generated using iterative morphology. Finally, head mask boundary coordinates are corrected accordingly to fully cover the head portion. Findings: The result section shows that the proposed method is more effective on normal brain MRI than skull stripped MRIs. The proposed method performs well in comparison with static morphology-based methods. For MRI with skull images, the score is 97.03% and for skull stripped MRI images the score is about 91%. In the case of brain CT, the proposed method achieved almost 90% Jaccard coefficient similarity index value. These results prove the efficiency and accuracy of the proposed method the results are compared with the existing well-known methods of Atkins et al. (IEEE Trans Med Imaging, 17(1), 98–107, https://doi.org/10.1109/42.668699, 1998), Liu et al. (EURASIP J Image Video Process, 2017(1), https://doi.org/10.1186/s13640-017-0209-y, 2017), Mol et al. (IOP Conf Ser Mater Sci Eng 396, 012039, https://doi.org/10.1088/1757-899x/396/1/012039, 2018), Chen et al. (Real-time patient table removal in CT images. Health Information Science, pp 1–8, Springer, https://doi.org/10.1007/978-3-319-48335-1_1, 2016) and Maureenvan Eijnatten et al. (Comput Methods Progr Biomed 208, 106261, https://doi.org/10.1016/j.cmpb.2021.106261, 2021). Novelty: The proposed method can remove any kind of background noises from both CT and MRI images. Here, the value of morphological operators is determined dynamically through iteration. Moreover, this work proposed a novel CT patient table removing approach from Brain CT images often are undesirable part of the medical image. CT background removing (dpeaa)DE-He213 Dynamic morphological operator (dpeaa)DE-He213 Head masking (dpeaa)DE-He213 MRI background removing (dpeaa)DE-He213 Sarkar, Kanishka (orcid)0000-0001-9695-894X aut Mandal, Ardhendu aut Biswas, Saroj Kr aut Enthalten in Proceedings of the Indian National Science Academy Indian National Science Academy, 2022 89(2023), 3 vom: 21. Juni, Seite 673-688 (DE-627)78783341X (DE-600)2773381-6 2454-9983 nnns volume:89 year:2023 number:3 day:21 month:06 pages:673-688 https://dx.doi.org/10.1007/s43538-023-00175-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 89 2023 3 21 06 673-688 |
allfieldsSound |
10.1007/s43538-023-00175-9 doi (DE-627)SPR052880206 (SPR)s43538-023-00175-9-e DE-627 ger DE-627 rakwb eng Halder, Tanmoy Kanti verfasserin aut A robust head MRI/CT background removing approach using dynamic morphological operations 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Indian National Science Academy 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Objectives: The objective of the work is to remove the CT table or other noises from Head CT/MRI images, which are often present and effectively decrease the efficiency of further intended image processing operations. Methods: At first, depending on the mass an adaptive ellipse-shaped mask is generated to segment out the Region of interest (ROI) area. Then an initial head mask has been generated using iterative morphology. Finally, head mask boundary coordinates are corrected accordingly to fully cover the head portion. Findings: The result section shows that the proposed method is more effective on normal brain MRI than skull stripped MRIs. The proposed method performs well in comparison with static morphology-based methods. For MRI with skull images, the score is 97.03% and for skull stripped MRI images the score is about 91%. In the case of brain CT, the proposed method achieved almost 90% Jaccard coefficient similarity index value. These results prove the efficiency and accuracy of the proposed method the results are compared with the existing well-known methods of Atkins et al. (IEEE Trans Med Imaging, 17(1), 98–107, https://doi.org/10.1109/42.668699, 1998), Liu et al. (EURASIP J Image Video Process, 2017(1), https://doi.org/10.1186/s13640-017-0209-y, 2017), Mol et al. (IOP Conf Ser Mater Sci Eng 396, 012039, https://doi.org/10.1088/1757-899x/396/1/012039, 2018), Chen et al. (Real-time patient table removal in CT images. Health Information Science, pp 1–8, Springer, https://doi.org/10.1007/978-3-319-48335-1_1, 2016) and Maureenvan Eijnatten et al. (Comput Methods Progr Biomed 208, 106261, https://doi.org/10.1016/j.cmpb.2021.106261, 2021). Novelty: The proposed method can remove any kind of background noises from both CT and MRI images. Here, the value of morphological operators is determined dynamically through iteration. Moreover, this work proposed a novel CT patient table removing approach from Brain CT images often are undesirable part of the medical image. CT background removing (dpeaa)DE-He213 Dynamic morphological operator (dpeaa)DE-He213 Head masking (dpeaa)DE-He213 MRI background removing (dpeaa)DE-He213 Sarkar, Kanishka (orcid)0000-0001-9695-894X aut Mandal, Ardhendu aut Biswas, Saroj Kr aut Enthalten in Proceedings of the Indian National Science Academy Indian National Science Academy, 2022 89(2023), 3 vom: 21. Juni, Seite 673-688 (DE-627)78783341X (DE-600)2773381-6 2454-9983 nnns volume:89 year:2023 number:3 day:21 month:06 pages:673-688 https://dx.doi.org/10.1007/s43538-023-00175-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 89 2023 3 21 06 673-688 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Objectives: The objective of the work is to remove the CT table or other noises from Head CT/MRI images, which are often present and effectively decrease the efficiency of further intended image processing operations. Methods: At first, depending on the mass an adaptive ellipse-shaped mask is generated to segment out the Region of interest (ROI) area. Then an initial head mask has been generated using iterative morphology. Finally, head mask boundary coordinates are corrected accordingly to fully cover the head portion. Findings: The result section shows that the proposed method is more effective on normal brain MRI than skull stripped MRIs. The proposed method performs well in comparison with static morphology-based methods. For MRI with skull images, the score is 97.03% and for skull stripped MRI images the score is about 91%. In the case of brain CT, the proposed method achieved almost 90% Jaccard coefficient similarity index value. These results prove the efficiency and accuracy of the proposed method the results are compared with the existing well-known methods of Atkins et al. (IEEE Trans Med Imaging, 17(1), 98–107, https://doi.org/10.1109/42.668699, 1998), Liu et al. (EURASIP J Image Video Process, 2017(1), https://doi.org/10.1186/s13640-017-0209-y, 2017), Mol et al. (IOP Conf Ser Mater Sci Eng 396, 012039, https://doi.org/10.1088/1757-899x/396/1/012039, 2018), Chen et al. (Real-time patient table removal in CT images. Health Information Science, pp 1–8, Springer, https://doi.org/10.1007/978-3-319-48335-1_1, 2016) and Maureenvan Eijnatten et al. (Comput Methods Progr Biomed 208, 106261, https://doi.org/10.1016/j.cmpb.2021.106261, 2021). Novelty: The proposed method can remove any kind of background noises from both CT and MRI images. Here, the value of morphological operators is determined dynamically through iteration. 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Halder, Tanmoy Kanti |
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Halder, Tanmoy Kanti misc CT background removing misc Dynamic morphological operator misc Head masking misc MRI background removing A robust head MRI/CT background removing approach using dynamic morphological operations |
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A robust head MRI/CT background removing approach using dynamic morphological operations CT background removing (dpeaa)DE-He213 Dynamic morphological operator (dpeaa)DE-He213 Head masking (dpeaa)DE-He213 MRI background removing (dpeaa)DE-He213 |
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A robust head MRI/CT background removing approach using dynamic morphological operations |
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robust head mri/ct background removing approach using dynamic morphological operations |
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A robust head MRI/CT background removing approach using dynamic morphological operations |
abstract |
Objectives: The objective of the work is to remove the CT table or other noises from Head CT/MRI images, which are often present and effectively decrease the efficiency of further intended image processing operations. Methods: At first, depending on the mass an adaptive ellipse-shaped mask is generated to segment out the Region of interest (ROI) area. Then an initial head mask has been generated using iterative morphology. Finally, head mask boundary coordinates are corrected accordingly to fully cover the head portion. Findings: The result section shows that the proposed method is more effective on normal brain MRI than skull stripped MRIs. The proposed method performs well in comparison with static morphology-based methods. For MRI with skull images, the score is 97.03% and for skull stripped MRI images the score is about 91%. In the case of brain CT, the proposed method achieved almost 90% Jaccard coefficient similarity index value. These results prove the efficiency and accuracy of the proposed method the results are compared with the existing well-known methods of Atkins et al. (IEEE Trans Med Imaging, 17(1), 98–107, https://doi.org/10.1109/42.668699, 1998), Liu et al. (EURASIP J Image Video Process, 2017(1), https://doi.org/10.1186/s13640-017-0209-y, 2017), Mol et al. (IOP Conf Ser Mater Sci Eng 396, 012039, https://doi.org/10.1088/1757-899x/396/1/012039, 2018), Chen et al. (Real-time patient table removal in CT images. Health Information Science, pp 1–8, Springer, https://doi.org/10.1007/978-3-319-48335-1_1, 2016) and Maureenvan Eijnatten et al. (Comput Methods Progr Biomed 208, 106261, https://doi.org/10.1016/j.cmpb.2021.106261, 2021). Novelty: The proposed method can remove any kind of background noises from both CT and MRI images. Here, the value of morphological operators is determined dynamically through iteration. Moreover, this work proposed a novel CT patient table removing approach from Brain CT images often are undesirable part of the medical image. © Indian National Science Academy 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
abstractGer |
Objectives: The objective of the work is to remove the CT table or other noises from Head CT/MRI images, which are often present and effectively decrease the efficiency of further intended image processing operations. Methods: At first, depending on the mass an adaptive ellipse-shaped mask is generated to segment out the Region of interest (ROI) area. Then an initial head mask has been generated using iterative morphology. Finally, head mask boundary coordinates are corrected accordingly to fully cover the head portion. Findings: The result section shows that the proposed method is more effective on normal brain MRI than skull stripped MRIs. The proposed method performs well in comparison with static morphology-based methods. For MRI with skull images, the score is 97.03% and for skull stripped MRI images the score is about 91%. In the case of brain CT, the proposed method achieved almost 90% Jaccard coefficient similarity index value. These results prove the efficiency and accuracy of the proposed method the results are compared with the existing well-known methods of Atkins et al. (IEEE Trans Med Imaging, 17(1), 98–107, https://doi.org/10.1109/42.668699, 1998), Liu et al. (EURASIP J Image Video Process, 2017(1), https://doi.org/10.1186/s13640-017-0209-y, 2017), Mol et al. (IOP Conf Ser Mater Sci Eng 396, 012039, https://doi.org/10.1088/1757-899x/396/1/012039, 2018), Chen et al. (Real-time patient table removal in CT images. Health Information Science, pp 1–8, Springer, https://doi.org/10.1007/978-3-319-48335-1_1, 2016) and Maureenvan Eijnatten et al. (Comput Methods Progr Biomed 208, 106261, https://doi.org/10.1016/j.cmpb.2021.106261, 2021). Novelty: The proposed method can remove any kind of background noises from both CT and MRI images. Here, the value of morphological operators is determined dynamically through iteration. Moreover, this work proposed a novel CT patient table removing approach from Brain CT images often are undesirable part of the medical image. © Indian National Science Academy 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
abstract_unstemmed |
Objectives: The objective of the work is to remove the CT table or other noises from Head CT/MRI images, which are often present and effectively decrease the efficiency of further intended image processing operations. Methods: At first, depending on the mass an adaptive ellipse-shaped mask is generated to segment out the Region of interest (ROI) area. Then an initial head mask has been generated using iterative morphology. Finally, head mask boundary coordinates are corrected accordingly to fully cover the head portion. Findings: The result section shows that the proposed method is more effective on normal brain MRI than skull stripped MRIs. The proposed method performs well in comparison with static morphology-based methods. For MRI with skull images, the score is 97.03% and for skull stripped MRI images the score is about 91%. In the case of brain CT, the proposed method achieved almost 90% Jaccard coefficient similarity index value. These results prove the efficiency and accuracy of the proposed method the results are compared with the existing well-known methods of Atkins et al. (IEEE Trans Med Imaging, 17(1), 98–107, https://doi.org/10.1109/42.668699, 1998), Liu et al. (EURASIP J Image Video Process, 2017(1), https://doi.org/10.1186/s13640-017-0209-y, 2017), Mol et al. (IOP Conf Ser Mater Sci Eng 396, 012039, https://doi.org/10.1088/1757-899x/396/1/012039, 2018), Chen et al. (Real-time patient table removal in CT images. Health Information Science, pp 1–8, Springer, https://doi.org/10.1007/978-3-319-48335-1_1, 2016) and Maureenvan Eijnatten et al. (Comput Methods Progr Biomed 208, 106261, https://doi.org/10.1016/j.cmpb.2021.106261, 2021). Novelty: The proposed method can remove any kind of background noises from both CT and MRI images. Here, the value of morphological operators is determined dynamically through iteration. Moreover, this work proposed a novel CT patient table removing approach from Brain CT images often are undesirable part of the medical image. © Indian National Science Academy 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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container_issue |
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title_short |
A robust head MRI/CT background removing approach using dynamic morphological operations |
url |
https://dx.doi.org/10.1007/s43538-023-00175-9 |
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author2 |
Sarkar, Kanishka Mandal, Ardhendu Biswas, Saroj Kr |
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Sarkar, Kanishka Mandal, Ardhendu Biswas, Saroj Kr |
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doi_str |
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up_date |
2024-07-03T15:26:46.447Z |
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score |
7.3989544 |