A novel technique for dental radiographic image segmentation based on neutrosophic logic

Oral diseases are very prevalent worldwide and affect people of all ages. Dentists depend on x-rays to study the characteristics of oral diseases. The segmentation and analysis of dental X-ray images pose various challenges compared to other medical images. This makes dental X-ray imaging more chall...
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Gespeichert in:

Autor*in:	Datta, Soma [verfasserIn] Chaki, Nabendu [verfasserIn] Modak, Biswajit [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Rechteinformationen:	Open Access Namensnennung 4.0 International ; CC BY 4.0

Schlagwörter:	Dental radiograph Fuzzy C-Means Neutrosophic image Neutrosophic logic Segmentation

Übergeordnetes Werk:	Enthalten in: Decision analytics journal - Amsterdam : Elsevier, 2021, 7(2023) vom: Juni, Artikel-ID 100223, Seite 1-13
Übergeordnetes Werk:	volume:7 ; year:2023 ; month:06 ; elocationid:100223 ; pages:1-13

Links:	Link aufrufen Link aufrufen

DOI / URN:	10.1016/j.dajour.2023.100223

Katalog-ID:	1872336272

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allfields	10.1016/j.dajour.2023.100223 doi (DE-627)1872336272 (DE-599)KXP1872336272 DE-627 ger DE-627 rda eng Datta, Soma verfasserin (DE-588)1312641355 (DE-627)1872336957 aut A novel technique for dental radiographic image segmentation based on neutrosophic logic Soma Datta, Nabendu Chaki, Biswajit Modak 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Oral diseases are very prevalent worldwide and affect people of all ages. Dentists depend on x-rays to study the characteristics of oral diseases. The segmentation and analysis of dental X-ray images pose various challenges compared to other medical images. This makes dental X-ray imaging more challenging because of poor resolution, which makes the segmentation of different parts of teeth and their abnormalities unreliable. It has been shown that Dental X-ray Image Segmentation (DXIS) is a primary and critical step in obtaining relevant and important details about oral diseases. DXIS plays a crucial role in practical dentistry to help identify various periodontal diseases. The proposed methodology automatically segments the teeth regions and assists in further analysis. It works on both peri-apical and panoramic types of dental radiographic images. Neutrosophic logic is used to select the initial region of interest. The best way to improve the performance and make the system faster is to restrict the computation within the foreground regions. The input dental radiographic image is mapped into the neutrosophic domain using the patch level feature, gradient feature, entropy feature, and local binary pattern. Applying neutrosophic logic helps to localize the initial region of interest. Subsequently, a fuzzy c-means algorithm is applied to segment a more accurate region of interest. The proposed methodology has been evaluated on publicly available data sets, 'Panoramic Dental X-rays with Segmented Mandibles' and 'Digital Dental X-ray Database for Caries Screening,' with the result that the accuracy of the proposed methodology is as high as 93.20%. This performance level confirms that the proposed segmentation technique highly correlates with the manual system. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Dental radiograph (dpeaa)DE-206 Fuzzy C-Means (dpeaa)DE-206 Neutrosophic image (dpeaa)DE-206 Neutrosophic logic (dpeaa)DE-206 Segmentation (dpeaa)DE-206 Chaki, Nabendu verfasserin (DE-588)1228927227 (DE-627)1750871602 aut Modak, Biswajit verfasserin aut Enthalten in Decision analytics journal Amsterdam : Elsevier, 2021 7(2023) vom: Juni, Artikel-ID 100223, Seite 1-13 Online-Ressource (DE-627)178621072X (DE-600)3106160-6 2772-6622 nnns volume:7 year:2023 month:06 elocationid:100223 pages:1-13 https://www.sciencedirect.com/science/article/pii/S2772662223000632/pdf Verlag kostenfrei https://doi.org/10.1016/j.dajour.2023.100223 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 7 2023 6 100223 1-13 26 01 0206 4429012679 x1z 08-12-23 2403 01 DE-LFER 4451700604 00 --%%-- --%%-- n --%%-- l01 09-01-24 2403 01 DE-LFER https://doi.org/10.1016/j.dajour.2023.100223 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2772662223000632/pdf
spelling	10.1016/j.dajour.2023.100223 doi (DE-627)1872336272 (DE-599)KXP1872336272 DE-627 ger DE-627 rda eng Datta, Soma verfasserin (DE-588)1312641355 (DE-627)1872336957 aut A novel technique for dental radiographic image segmentation based on neutrosophic logic Soma Datta, Nabendu Chaki, Biswajit Modak 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Oral diseases are very prevalent worldwide and affect people of all ages. Dentists depend on x-rays to study the characteristics of oral diseases. The segmentation and analysis of dental X-ray images pose various challenges compared to other medical images. This makes dental X-ray imaging more challenging because of poor resolution, which makes the segmentation of different parts of teeth and their abnormalities unreliable. It has been shown that Dental X-ray Image Segmentation (DXIS) is a primary and critical step in obtaining relevant and important details about oral diseases. DXIS plays a crucial role in practical dentistry to help identify various periodontal diseases. The proposed methodology automatically segments the teeth regions and assists in further analysis. It works on both peri-apical and panoramic types of dental radiographic images. Neutrosophic logic is used to select the initial region of interest. The best way to improve the performance and make the system faster is to restrict the computation within the foreground regions. The input dental radiographic image is mapped into the neutrosophic domain using the patch level feature, gradient feature, entropy feature, and local binary pattern. Applying neutrosophic logic helps to localize the initial region of interest. Subsequently, a fuzzy c-means algorithm is applied to segment a more accurate region of interest. The proposed methodology has been evaluated on publicly available data sets, 'Panoramic Dental X-rays with Segmented Mandibles' and 'Digital Dental X-ray Database for Caries Screening,' with the result that the accuracy of the proposed methodology is as high as 93.20%. This performance level confirms that the proposed segmentation technique highly correlates with the manual system. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Dental radiograph (dpeaa)DE-206 Fuzzy C-Means (dpeaa)DE-206 Neutrosophic image (dpeaa)DE-206 Neutrosophic logic (dpeaa)DE-206 Segmentation (dpeaa)DE-206 Chaki, Nabendu verfasserin (DE-588)1228927227 (DE-627)1750871602 aut Modak, Biswajit verfasserin aut Enthalten in Decision analytics journal Amsterdam : Elsevier, 2021 7(2023) vom: Juni, Artikel-ID 100223, Seite 1-13 Online-Ressource (DE-627)178621072X (DE-600)3106160-6 2772-6622 nnns volume:7 year:2023 month:06 elocationid:100223 pages:1-13 https://www.sciencedirect.com/science/article/pii/S2772662223000632/pdf Verlag kostenfrei https://doi.org/10.1016/j.dajour.2023.100223 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 7 2023 6 100223 1-13 26 01 0206 4429012679 x1z 08-12-23 2403 01 DE-LFER 4451700604 00 --%%-- --%%-- n --%%-- l01 09-01-24 2403 01 DE-LFER https://doi.org/10.1016/j.dajour.2023.100223 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2772662223000632/pdf
allfields_unstemmed	10.1016/j.dajour.2023.100223 doi (DE-627)1872336272 (DE-599)KXP1872336272 DE-627 ger DE-627 rda eng Datta, Soma verfasserin (DE-588)1312641355 (DE-627)1872336957 aut A novel technique for dental radiographic image segmentation based on neutrosophic logic Soma Datta, Nabendu Chaki, Biswajit Modak 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Oral diseases are very prevalent worldwide and affect people of all ages. Dentists depend on x-rays to study the characteristics of oral diseases. The segmentation and analysis of dental X-ray images pose various challenges compared to other medical images. This makes dental X-ray imaging more challenging because of poor resolution, which makes the segmentation of different parts of teeth and their abnormalities unreliable. It has been shown that Dental X-ray Image Segmentation (DXIS) is a primary and critical step in obtaining relevant and important details about oral diseases. DXIS plays a crucial role in practical dentistry to help identify various periodontal diseases. The proposed methodology automatically segments the teeth regions and assists in further analysis. It works on both peri-apical and panoramic types of dental radiographic images. Neutrosophic logic is used to select the initial region of interest. The best way to improve the performance and make the system faster is to restrict the computation within the foreground regions. The input dental radiographic image is mapped into the neutrosophic domain using the patch level feature, gradient feature, entropy feature, and local binary pattern. Applying neutrosophic logic helps to localize the initial region of interest. Subsequently, a fuzzy c-means algorithm is applied to segment a more accurate region of interest. The proposed methodology has been evaluated on publicly available data sets, 'Panoramic Dental X-rays with Segmented Mandibles' and 'Digital Dental X-ray Database for Caries Screening,' with the result that the accuracy of the proposed methodology is as high as 93.20%. This performance level confirms that the proposed segmentation technique highly correlates with the manual system. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Dental radiograph (dpeaa)DE-206 Fuzzy C-Means (dpeaa)DE-206 Neutrosophic image (dpeaa)DE-206 Neutrosophic logic (dpeaa)DE-206 Segmentation (dpeaa)DE-206 Chaki, Nabendu verfasserin (DE-588)1228927227 (DE-627)1750871602 aut Modak, Biswajit verfasserin aut Enthalten in Decision analytics journal Amsterdam : Elsevier, 2021 7(2023) vom: Juni, Artikel-ID 100223, Seite 1-13 Online-Ressource (DE-627)178621072X (DE-600)3106160-6 2772-6622 nnns volume:7 year:2023 month:06 elocationid:100223 pages:1-13 https://www.sciencedirect.com/science/article/pii/S2772662223000632/pdf Verlag kostenfrei https://doi.org/10.1016/j.dajour.2023.100223 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 7 2023 6 100223 1-13 26 01 0206 4429012679 x1z 08-12-23 2403 01 DE-LFER 4451700604 00 --%%-- --%%-- n --%%-- l01 09-01-24 2403 01 DE-LFER https://doi.org/10.1016/j.dajour.2023.100223 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2772662223000632/pdf
allfieldsGer	10.1016/j.dajour.2023.100223 doi (DE-627)1872336272 (DE-599)KXP1872336272 DE-627 ger DE-627 rda eng Datta, Soma verfasserin (DE-588)1312641355 (DE-627)1872336957 aut A novel technique for dental radiographic image segmentation based on neutrosophic logic Soma Datta, Nabendu Chaki, Biswajit Modak 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Oral diseases are very prevalent worldwide and affect people of all ages. Dentists depend on x-rays to study the characteristics of oral diseases. The segmentation and analysis of dental X-ray images pose various challenges compared to other medical images. This makes dental X-ray imaging more challenging because of poor resolution, which makes the segmentation of different parts of teeth and their abnormalities unreliable. It has been shown that Dental X-ray Image Segmentation (DXIS) is a primary and critical step in obtaining relevant and important details about oral diseases. DXIS plays a crucial role in practical dentistry to help identify various periodontal diseases. The proposed methodology automatically segments the teeth regions and assists in further analysis. It works on both peri-apical and panoramic types of dental radiographic images. Neutrosophic logic is used to select the initial region of interest. The best way to improve the performance and make the system faster is to restrict the computation within the foreground regions. The input dental radiographic image is mapped into the neutrosophic domain using the patch level feature, gradient feature, entropy feature, and local binary pattern. Applying neutrosophic logic helps to localize the initial region of interest. Subsequently, a fuzzy c-means algorithm is applied to segment a more accurate region of interest. The proposed methodology has been evaluated on publicly available data sets, 'Panoramic Dental X-rays with Segmented Mandibles' and 'Digital Dental X-ray Database for Caries Screening,' with the result that the accuracy of the proposed methodology is as high as 93.20%. This performance level confirms that the proposed segmentation technique highly correlates with the manual system. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Dental radiograph (dpeaa)DE-206 Fuzzy C-Means (dpeaa)DE-206 Neutrosophic image (dpeaa)DE-206 Neutrosophic logic (dpeaa)DE-206 Segmentation (dpeaa)DE-206 Chaki, Nabendu verfasserin (DE-588)1228927227 (DE-627)1750871602 aut Modak, Biswajit verfasserin aut Enthalten in Decision analytics journal Amsterdam : Elsevier, 2021 7(2023) vom: Juni, Artikel-ID 100223, Seite 1-13 Online-Ressource (DE-627)178621072X (DE-600)3106160-6 2772-6622 nnns volume:7 year:2023 month:06 elocationid:100223 pages:1-13 https://www.sciencedirect.com/science/article/pii/S2772662223000632/pdf Verlag kostenfrei https://doi.org/10.1016/j.dajour.2023.100223 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 7 2023 6 100223 1-13 26 01 0206 4429012679 x1z 08-12-23 2403 01 DE-LFER 4451700604 00 --%%-- --%%-- n --%%-- l01 09-01-24 2403 01 DE-LFER https://doi.org/10.1016/j.dajour.2023.100223 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2772662223000632/pdf
allfieldsSound	10.1016/j.dajour.2023.100223 doi (DE-627)1872336272 (DE-599)KXP1872336272 DE-627 ger DE-627 rda eng Datta, Soma verfasserin (DE-588)1312641355 (DE-627)1872336957 aut A novel technique for dental radiographic image segmentation based on neutrosophic logic Soma Datta, Nabendu Chaki, Biswajit Modak 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Oral diseases are very prevalent worldwide and affect people of all ages. Dentists depend on x-rays to study the characteristics of oral diseases. The segmentation and analysis of dental X-ray images pose various challenges compared to other medical images. This makes dental X-ray imaging more challenging because of poor resolution, which makes the segmentation of different parts of teeth and their abnormalities unreliable. It has been shown that Dental X-ray Image Segmentation (DXIS) is a primary and critical step in obtaining relevant and important details about oral diseases. DXIS plays a crucial role in practical dentistry to help identify various periodontal diseases. The proposed methodology automatically segments the teeth regions and assists in further analysis. It works on both peri-apical and panoramic types of dental radiographic images. Neutrosophic logic is used to select the initial region of interest. The best way to improve the performance and make the system faster is to restrict the computation within the foreground regions. The input dental radiographic image is mapped into the neutrosophic domain using the patch level feature, gradient feature, entropy feature, and local binary pattern. Applying neutrosophic logic helps to localize the initial region of interest. Subsequently, a fuzzy c-means algorithm is applied to segment a more accurate region of interest. The proposed methodology has been evaluated on publicly available data sets, 'Panoramic Dental X-rays with Segmented Mandibles' and 'Digital Dental X-ray Database for Caries Screening,' with the result that the accuracy of the proposed methodology is as high as 93.20%. This performance level confirms that the proposed segmentation technique highly correlates with the manual system. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Dental radiograph (dpeaa)DE-206 Fuzzy C-Means (dpeaa)DE-206 Neutrosophic image (dpeaa)DE-206 Neutrosophic logic (dpeaa)DE-206 Segmentation (dpeaa)DE-206 Chaki, Nabendu verfasserin (DE-588)1228927227 (DE-627)1750871602 aut Modak, Biswajit verfasserin aut Enthalten in Decision analytics journal Amsterdam : Elsevier, 2021 7(2023) vom: Juni, Artikel-ID 100223, Seite 1-13 Online-Ressource (DE-627)178621072X (DE-600)3106160-6 2772-6622 nnns volume:7 year:2023 month:06 elocationid:100223 pages:1-13 https://www.sciencedirect.com/science/article/pii/S2772662223000632/pdf Verlag kostenfrei https://doi.org/10.1016/j.dajour.2023.100223 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 7 2023 6 100223 1-13 26 01 0206 4429012679 x1z 08-12-23 2403 01 DE-LFER 4451700604 00 --%%-- --%%-- n --%%-- l01 09-01-24 2403 01 DE-LFER https://doi.org/10.1016/j.dajour.2023.100223 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2772662223000632/pdf
language	English
source	Enthalten in Decision analytics journal 7(2023) vom: Juni, Artikel-ID 100223, Seite 1-13 volume:7 year:2023 month:06 elocationid:100223 pages:1-13
sourceStr	Enthalten in Decision analytics journal 7(2023) vom: Juni, Artikel-ID 100223, Seite 1-13 volume:7 year:2023 month:06 elocationid:100223 pages:1-13
format_phy_str_mv	Article
building	26:1 2403:0
institution	findex.gbv.de
selectbib_iln_str_mv	26@1z 2403@01
topic_facet	Dental radiograph Fuzzy C-Means Neutrosophic image Neutrosophic logic Segmentation
isfreeaccess_bool	true
container_title	Decision analytics journal
authorswithroles_txt_mv	Datta, Soma @@aut@@ Chaki, Nabendu @@aut@@ Modak, Biswajit @@aut@@
publishDateDaySort_date	2023-06-01T00:00:00Z
hierarchy_top_id	178621072X
id	1872336272
language_de	englisch
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spellingShingle	Datta, Soma misc Dental radiograph misc Fuzzy C-Means misc Neutrosophic image misc Neutrosophic logic misc Segmentation A novel technique for dental radiographic image segmentation based on neutrosophic logic
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topic_title	A novel technique for dental radiographic image segmentation based on neutrosophic logic Soma Datta, Nabendu Chaki, Biswajit Modak Dental radiograph (dpeaa)DE-206 Fuzzy C-Means (dpeaa)DE-206 Neutrosophic image (dpeaa)DE-206 Neutrosophic logic (dpeaa)DE-206 Segmentation (dpeaa)DE-206
topic	misc Dental radiograph misc Fuzzy C-Means misc Neutrosophic image misc Neutrosophic logic misc Segmentation
topic_unstemmed	misc Dental radiograph misc Fuzzy C-Means misc Neutrosophic image misc Neutrosophic logic misc Segmentation
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title	A novel technique for dental radiographic image segmentation based on neutrosophic logic
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title_full	A novel technique for dental radiographic image segmentation based on neutrosophic logic Soma Datta, Nabendu Chaki, Biswajit Modak
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title_sort	novel technique for dental radiographic image segmentation based on neutrosophic logic
title_auth	A novel technique for dental radiographic image segmentation based on neutrosophic logic
abstract	Oral diseases are very prevalent worldwide and affect people of all ages. Dentists depend on x-rays to study the characteristics of oral diseases. The segmentation and analysis of dental X-ray images pose various challenges compared to other medical images. This makes dental X-ray imaging more challenging because of poor resolution, which makes the segmentation of different parts of teeth and their abnormalities unreliable. It has been shown that Dental X-ray Image Segmentation (DXIS) is a primary and critical step in obtaining relevant and important details about oral diseases. DXIS plays a crucial role in practical dentistry to help identify various periodontal diseases. The proposed methodology automatically segments the teeth regions and assists in further analysis. It works on both peri-apical and panoramic types of dental radiographic images. Neutrosophic logic is used to select the initial region of interest. The best way to improve the performance and make the system faster is to restrict the computation within the foreground regions. The input dental radiographic image is mapped into the neutrosophic domain using the patch level feature, gradient feature, entropy feature, and local binary pattern. Applying neutrosophic logic helps to localize the initial region of interest. Subsequently, a fuzzy c-means algorithm is applied to segment a more accurate region of interest. The proposed methodology has been evaluated on publicly available data sets, 'Panoramic Dental X-rays with Segmented Mandibles' and 'Digital Dental X-ray Database for Caries Screening,' with the result that the accuracy of the proposed methodology is as high as 93.20%. This performance level confirms that the proposed segmentation technique highly correlates with the manual system.
abstractGer	Oral diseases are very prevalent worldwide and affect people of all ages. Dentists depend on x-rays to study the characteristics of oral diseases. The segmentation and analysis of dental X-ray images pose various challenges compared to other medical images. This makes dental X-ray imaging more challenging because of poor resolution, which makes the segmentation of different parts of teeth and their abnormalities unreliable. It has been shown that Dental X-ray Image Segmentation (DXIS) is a primary and critical step in obtaining relevant and important details about oral diseases. DXIS plays a crucial role in practical dentistry to help identify various periodontal diseases. The proposed methodology automatically segments the teeth regions and assists in further analysis. It works on both peri-apical and panoramic types of dental radiographic images. Neutrosophic logic is used to select the initial region of interest. The best way to improve the performance and make the system faster is to restrict the computation within the foreground regions. The input dental radiographic image is mapped into the neutrosophic domain using the patch level feature, gradient feature, entropy feature, and local binary pattern. Applying neutrosophic logic helps to localize the initial region of interest. Subsequently, a fuzzy c-means algorithm is applied to segment a more accurate region of interest. The proposed methodology has been evaluated on publicly available data sets, 'Panoramic Dental X-rays with Segmented Mandibles' and 'Digital Dental X-ray Database for Caries Screening,' with the result that the accuracy of the proposed methodology is as high as 93.20%. This performance level confirms that the proposed segmentation technique highly correlates with the manual system.
abstract_unstemmed	Oral diseases are very prevalent worldwide and affect people of all ages. Dentists depend on x-rays to study the characteristics of oral diseases. The segmentation and analysis of dental X-ray images pose various challenges compared to other medical images. This makes dental X-ray imaging more challenging because of poor resolution, which makes the segmentation of different parts of teeth and their abnormalities unreliable. It has been shown that Dental X-ray Image Segmentation (DXIS) is a primary and critical step in obtaining relevant and important details about oral diseases. DXIS plays a crucial role in practical dentistry to help identify various periodontal diseases. The proposed methodology automatically segments the teeth regions and assists in further analysis. It works on both peri-apical and panoramic types of dental radiographic images. Neutrosophic logic is used to select the initial region of interest. The best way to improve the performance and make the system faster is to restrict the computation within the foreground regions. The input dental radiographic image is mapped into the neutrosophic domain using the patch level feature, gradient feature, entropy feature, and local binary pattern. Applying neutrosophic logic helps to localize the initial region of interest. Subsequently, a fuzzy c-means algorithm is applied to segment a more accurate region of interest. The proposed methodology has been evaluated on publicly available data sets, 'Panoramic Dental X-rays with Segmented Mandibles' and 'Digital Dental X-ray Database for Caries Screening,' with the result that the accuracy of the proposed methodology is as high as 93.20%. This performance level confirms that the proposed segmentation technique highly correlates with the manual system.
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title_short	A novel technique for dental radiographic image segmentation based on neutrosophic logic
url	https://www.sciencedirect.com/science/article/pii/S2772662223000632/pdf https://doi.org/10.1016/j.dajour.2023.100223
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a2200265 4500</leader><controlfield tag="001">1872336272</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231208095728.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">231208s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.dajour.2023.100223</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)1872336272</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KXP1872336272</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Datta, Soma</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1312641355</subfield><subfield code="0">(DE-627)1872336957</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A novel technique for dental radiographic image segmentation based on neutrosophic logic</subfield><subfield code="c">Soma Datta, Nabendu Chaki, Biswajit Modak</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="506" ind1="0" ind2=" "><subfield code="q">DE-206</subfield><subfield code="a">Open Access</subfield><subfield code="e">Controlled Vocabulary for Access Rights</subfield><subfield code="u">http://purl.org/coar/access_right/c_abf2</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Oral diseases are very prevalent worldwide and affect people of all ages. Dentists depend on x-rays to study the characteristics of oral diseases. The segmentation and analysis of dental X-ray images pose various challenges compared to other medical images. This makes dental X-ray imaging more challenging because of poor resolution, which makes the segmentation of different parts of teeth and their abnormalities unreliable. It has been shown that Dental X-ray Image Segmentation (DXIS) is a primary and critical step in obtaining relevant and important details about oral diseases. DXIS plays a crucial role in practical dentistry to help identify various periodontal diseases. The proposed methodology automatically segments the teeth regions and assists in further analysis. It works on both peri-apical and panoramic types of dental radiographic images. Neutrosophic logic is used to select the initial region of interest. The best way to improve the performance and make the system faster is to restrict the computation within the foreground regions. The input dental radiographic image is mapped into the neutrosophic domain using the patch level feature, gradient feature, entropy feature, and local binary pattern. Applying neutrosophic logic helps to localize the initial region of interest. Subsequently, a fuzzy c-means algorithm is applied to segment a more accurate region of interest. The proposed methodology has been evaluated on publicly available data sets, 'Panoramic Dental X-rays with Segmented Mandibles' and 'Digital Dental X-ray Database for Caries Screening,' with the result that the accuracy of the proposed methodology is as high as 93.20%. 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A novel technique for dental radiographic image segmentation based on neutrosophic logic

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