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Effects of Contrast Limited Adaptive Histogram Equalization (CLAHE) on Manual and Automated Tracing of Lateral Cephalometric Radiographs

Year 2024, Volume: 14 Issue: 3, 733 - 744, 30.09.2024
https://doi.org/10.33808/clinexphealthsci.1357008

Abstract

Objective: The aim of this study is to compare the difference between original lateral cephalometric radiographs (LCRs) and Contrast Limited Adaptive Histogram Equalization (CLAHE) LCRs in two examiners and WebCeph.
Methods: A total of 200 LCRs were selected, and CLAHE (tile size: 20*20) was applied to the original LCRs. 27 LCR landmarks were manually determined by two examiners and, the selected LCR’s determined automatically using the WebCeph program. Absolute differences between the original LCRs and CLAHE-LCRs were calculated in the x-y axes and Euclidean distance. The Kruskal Wallis test was used for comparisons
between the examiners and WebCeph. The Wilcoxon Signed Rank Test was used to evaluate the x and y axes within each group.
Results: The best accuracy values were seen in examiner 1 along the x-y axes and Euclidean distance, while the worst accuracy values were seen in WebCeph. The mean differences according to the methods were higher along the y-axis than along the x-axis for both examiners (except PNS, Me’) and WebCeph (except Po, Co). The mean Euclidean distances were above 2 mm only in Co, PNS at Examiner 1, PNS, Po, Ba, Co, Go, Pog, U1RT, Me’, Pog at Examiner 2, and WebCeph in all measurements. However, the differences in Euclidean distances were less than 4 mm for both examiners and WebCeph.
Conclusion: CLAHE-LCRs require more adjustments for landmark determination in WebCeph than the in the manual system.

Ethical Statement

This study was approved by the Ethics Committee of the Recep Tayyip Erdogan University Faculty of Medicine (30/09/2021-2021/168)

References

  • Devereux L, Moles D, Cunningham SJ, McKnight M. How important are lateral cephalometric radiographs in orthodontic treatment planning?. Am J Orthod Dentofacial Orthop. 2011;139(2):e175-e181. DOI: 10.1016/j.ajodo.2010.09.021.
  • Junaid N, Khan N, Ahmed N, Abbasi MS, Das G, Maqsood A, Ahmed AR, Marya A, Alam MK, Heboyan A. Development, application, and performance of artificial intelligence in cephalometric landmark identification and diagnosis: A systematic review. Healthcare (Basel). 2022;10(12):2454. DOI: 10.3390/healthcare10122454.
  • Wong SH, Al-Hasani H, Alam Z, Alam A. Artificial intelligence in radiology: How will we be affected?. Eur Radiol. 2019;29(1):141-143. DOI: 10.1007/s00330-018-5644-3.
  • Ahmed N, Abbasi MS, Zuberi F, Qamar W, Halim MSB, Maqsood A, Alam MK. Artificial intelligence techniques: Analysis, application, and outcome in dentistry-a systematic review. Biomed Res Int. 2021;2021:1-15. DOI: 10.1155/2021/9751564.
  • Nguyen TT, Larrivée N, Lee A, Bilaniuk O, Durand R. Use of artificial intelligence in dentistry: Current clinical trends and research advances. J Can Dent Assoc. 2021;87:l7.
  • Kiełczykowski M, Kamiński K, Perkowski K, Zadurska M, Czochrowska E. Application of artificial intelligence (ai) in a cephalometric analysis: A narrative review. Diagnostics (Basel). 2023;13(16):2640. DOI: 10.3390/diagnostics13162640.
  • Kim H, Shim E, Park J, Kim YJ, Lee U, Kim Y. Web-based fully automated cephalometric analysis by deep learning. Comput Methods Programs Biomed. 2020;194:105513. DOI: 10.1016/j.cmpb.2020.105513.
  • Houston WJ. The analysis of errors in orthodontic measurements. Am J Orthod. 1983;83(5):382-390. DOI: 10.1016/0002-9416(83)90322-6.
  • Midtgård J, Björk G, Linder-Aronson ST. Reproducibility of cephalometric landmarks and errors of measurements of cephalometric cranial distances. Angle Orthod. 1974;44(1):56-61. DOI: 10.1043/0003-3219(1974)044<0056:ROCLAE>2.0.CO;2.
  • Eppley BL, Sadove AM. Computerized digital enhancement in craniofacial cephalometric radiography. J Oral Maxillofac Surg. 1991;49(10):1038-1043. DOI: 10.1016/0278-2391(91)90133-7.
  • Ismail WZ, Sim KS. Contrast enhancement dynamic histogram equalization for medical image processing application. Int J Imaging Syst Technol. 2011;21(3):280-289. DOI: 10.1002/ima.20295.
  • Zuiderveld K. Contrast limited adaptive histogram equalization. In: Heckbert PS, editor. Graphics Gems IV. Cambridge, MA: Academic Press; 1994.p.474-485.
  • Chung M, Lee J, Park S, Lee M, Lee CE, Lee J, Shin YG. Individual tooth detection and identification from dental panoramic x-ray images via point-wise localization and distance regularization. Artif Intell Med. 2021;111:101996. DOI: 10.1016/j.artmed.2020.101996.
  • Rashmi S, Murthy P, Ashok V, Srinath S. Cephalometric skeletal structure classification using convolutional neural networks and heatmap regression. SN Comput Sci. 2022;3(5):336. DOI: 10.1007/s42979-022-01230-w
  • Pizer SM, Amburn EP, Austin JD, Cromatie R, Geselowitz A, Greer T, Romeny BH, Zimmerman JB, Zuiderveld K. Adaptive histogram equalization and its variations. Comput Vision Graph. 1987;39(3):355-368. DOI: 10.1016/S0734-189x(87)80186-X.
  • Qassim HM, Basheer NM, Farhan MN. Brightness preserving enhancement for dental digital x-ray images based on entropy and histogram analysis. J Appl Sci Eng. 2019;22(1):187-194. DOI: 10.6180/jase.201903_22(1).0019.
  • Pandyan UM, Arumugam B, Gurunathan U, Kopuli Ashkar Ali SH. Automatic localization of inferior alveolar nerve canal in panoramic dental images. Signal Image Video P. 2022;16(5):1389-1397. DOI: 10.1007/s11760-021-02091-1.
  • Faul F, Erdfelder E, Lang AG, Buchner A. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods. 2007;39(2):175-191. DOI: 10.3758/bf03193146.
  • Koo TK, Li MY. A Guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med. 2016;15(2):155-163. DOI: 10.1016/j.jcm.2016.02.012.
  • Rossmann K, Wiley BE. The central problem in the study of radiographic image quality. Radiology. 1970;96(1):113-118. DOI: 10.1148/96.1.113.
  • McWilliam JS, Welander U. The effect of image quality on the identification of cephalometric landmarks. Angle Orthod. 1978;48(1):49-56. DOI: 10.1043/0003-3219(1978)048<0049:TEOIQO>2.0.CO;2.
  • Döler W, Steinhöfel N, Jäger A. Digital image processing techniques for cephalometric analysis. Comput Biol Med. 1991;21(1-2):23-33. DOI: 10.1016/0010-4825(91)90032-5.
  • McClure SR, Sadowsky PL, Ferreira A, Jacobson A. Reliability of digital versus conventional cephalometric radiology: A comparative evaluation of landmark identification error. Semin Orthod. 2005;11(2):98-110. DOI: 10.1053/j.sodo.2005.04.002.
  • Chen YJ, Chen SK, Yao JC, Chang HF. The effects of differences in landmark identification on the cephalometric measurements in traditional versus digitized cephalometry. Angle Orthod. 2004;74(2):155-161. DOI: 10.1043/0003-3219(2004)074<0155:TEODIL>2.0.CO;2.
  • Turner PJ, Weerakone S. An evaluation of the reproducibility of landmark identification using scanned cephalometric images. J Orthod. 2001;28(3):221-230. DOI: 10.1093/ortho/28.3.221.
  • Oshagh M, Shahidi SH, Danaei SM. Effects of image enhancement on reliability of landmark identification in digital cephalometry. Indian J Dent Res. 2013;24(1):98-103. DOI: 10.4103/0970-9290.114958.
  • Duarte H, Vieck R, Siqueira DF, Angelieri F, Bommarito S, Dalben G, Sannomiya EK. Effect of image compression of digital lateral cephalograms on the reproducibility of cephalometric points. Dentomaxillofac Radiol. 2009;38(6):393-400. DOI: 10.1259/dmfr/40996636.
  • Nikneshan S, Mohseni S, Nouri M, Hadian H, Kharazifard MJ. The effect of emboss enhancement on reliability of landmark identification in digital lateral cephalometric images. Iran J Radiol. 2015;12(2):e19302. DOI: 10.5812/iranjradiol.19302.
  • Leonardi R, Giordano D, Maiorana F, Spampinato C. Automatic cephalometric analysis. Angle Orthod. 2008;78(1):145-151. DOI: 10.2319/120506-491.1.
  • Wang CW, Huang CT, Hsieh MC, Li CH, Chang SW, Li WC, Vandaele R, Marée R, Jodogne S, Geurts P, Chen C, Zheng G, Chu C, Mirzaalian H, Hamarneh G, Vrtovec T, Ibragimov B. Evaluation and comparison of anatomical landmark detection methods for cephalometric x-ray images: A grand challenge. IEEE Trans Med Imaging. 2015;34(9):1890-1900. DOI: 10.1109/TMI.2015.2412951.
  • Wang CW, Huang CT, Lee JH, Li CH, Chang SW, Siao MJ, Lai TM, Ibragimov B, Vrtovec T, Ronneberger O, Fischer P, Cootes TF, Lindner C. A benchmark for comparison of dental radiography analysis algorithms. Med Image Anal. 2016;31:63-76. DOI: 10.1016/j.media.2016.02.004.
  • Park JH, Hwang HW, Moon JH, Yu Y, Kim H, Her SB, Srinivasan G, Aljanabi MNA, Donatelli RE, Lee SJ. Automated identification of cephalometric landmarks: Part 1-comparisons between the latest deep-learning methods YOLOV3 and SSD. Angle Orthod. 2019;89(6):903-909. DOI: 10.2319/022019-127.1.
  • Hwang HW, Park JH, Moon JH, Yu Y, Kim H, Her SB, Srinivasan G, Aljanabi MNA, Donatelli RE, Lee SJ. Automated identification of cephalometric landmarks: Part 2-might it be better than human? Angle Orthod. 2020;90(1):69-76. DOI: 10.2319/022019-129.1
  • Hwang HW, Moon JH, Kim MG, Donatelli RE, Lee SJ. Evaluation of automated cephalometric analysis based on the latest deep learning method. Angle Orthod. 2021;91(3):329-335. DOI: 10.2319/021220-100.1.
  • Kumari AR, Rao SN, Reddy PR. Design of hybrid dental caries segmentation and caries detection with meta-heuristic-based ResneXt-RNN. Biomed Signal Process Control. 2022;78:103961 DOI: 10.1016/j.bspc.2022.103961.
  • Georgieva VM, Mihaylova AD, Petrov PP. An application of dental x-ray image enhancement. In: Dimitrijević T, Stošić B, editors. 13th International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS); 2017 Oct 18-20; Nis, Serbia; 2017. pp.447-450.
  • Li W, Jiang X, Sun W, Wang SH, Liu C, Zhang X, Zhang YD, Zhou W, Miao L. Gingivitis identification via multichannel gray‐level co‐occurrence matrix and particle swarm optimization neural network. Int J Imaging Syst Technol. 2020;30(2):401-411. DOI: 10.1002/ima.22385.
  • Rahmi-Fajrin H, Puspita S, Riyadi S, Sofiani E. Dental radiography image enhancement for treatment evaluation through digital image processing. J Clin Exp Dent. 2018;10(7):e629-e634. DOI: 10.4317/jced.54607
  • Bhan A, Thakur A, Vyas G. Analysis of histogram based compound contrast enhancement with noise reduction method for endodontic therapy. 5th International Conference-Confluence The Next Generation Information Technology Summit (Confluence); 2014 Sept 25-26; Noida, India; 2014. pp.533-537.
  • Nishimoto S, Sotsuka Y, Kawai K, Ishise H, Kakibuchi M. Personal computer-based cephalometric landmark detection with deep learning, using cephalograms on the internet. J Craniofac Surg. 2019;30(1):91-95. DOI: 10.1097/SCS.0000000000004901.
  • Yao J, Zeng W, He T, Zhou S, Zhang Y, Guo J, Tang W. Automatic localization of cephalometric landmarks based on convolutional neural network. Am J Orthod Dentofacial Orthop. 2022;161(3):e250-e259. DOI: 10.1016/j.ajodo.2021.09.012.
  • Durão AP, Morosolli A, Pittayapat P, Bolstad N, Ferreira AP, Jacobs R. Cephalometric landmark variability among orthodontists and dentomaxillofacial radiologists: A comparative study. Imaging Sci Dent. 2015;45(4):213-220. DOI: 10.5624/isd.2015.45.4.213
  • Ha EG, Jeon KJ, Kim YH, Kim JY, Han SS. Automatic detection of mesiodens on panoramic radiographs using artificial intelligence. Sci Rep. 2021;11(1):23061. DOI: 10.1038/s41598-021-02571-x.
  • Menezes LD, Silva TP, Lima Dos Santos MA, Hughes MM, Mariano Souza SD, Leite Ribeiro PM, Freitas PH, Takeshita WM. Assessment of landmark detection in cephalometric radiographs with different conditions of brightness and contrast using the an artificial intelligence software. Dentomaxillofac Radiol. 2023;52(8):20230065. DOI: 10.1259/dmfr.20230065.
Year 2024, Volume: 14 Issue: 3, 733 - 744, 30.09.2024
https://doi.org/10.33808/clinexphealthsci.1357008

Abstract

References

  • Devereux L, Moles D, Cunningham SJ, McKnight M. How important are lateral cephalometric radiographs in orthodontic treatment planning?. Am J Orthod Dentofacial Orthop. 2011;139(2):e175-e181. DOI: 10.1016/j.ajodo.2010.09.021.
  • Junaid N, Khan N, Ahmed N, Abbasi MS, Das G, Maqsood A, Ahmed AR, Marya A, Alam MK, Heboyan A. Development, application, and performance of artificial intelligence in cephalometric landmark identification and diagnosis: A systematic review. Healthcare (Basel). 2022;10(12):2454. DOI: 10.3390/healthcare10122454.
  • Wong SH, Al-Hasani H, Alam Z, Alam A. Artificial intelligence in radiology: How will we be affected?. Eur Radiol. 2019;29(1):141-143. DOI: 10.1007/s00330-018-5644-3.
  • Ahmed N, Abbasi MS, Zuberi F, Qamar W, Halim MSB, Maqsood A, Alam MK. Artificial intelligence techniques: Analysis, application, and outcome in dentistry-a systematic review. Biomed Res Int. 2021;2021:1-15. DOI: 10.1155/2021/9751564.
  • Nguyen TT, Larrivée N, Lee A, Bilaniuk O, Durand R. Use of artificial intelligence in dentistry: Current clinical trends and research advances. J Can Dent Assoc. 2021;87:l7.
  • Kiełczykowski M, Kamiński K, Perkowski K, Zadurska M, Czochrowska E. Application of artificial intelligence (ai) in a cephalometric analysis: A narrative review. Diagnostics (Basel). 2023;13(16):2640. DOI: 10.3390/diagnostics13162640.
  • Kim H, Shim E, Park J, Kim YJ, Lee U, Kim Y. Web-based fully automated cephalometric analysis by deep learning. Comput Methods Programs Biomed. 2020;194:105513. DOI: 10.1016/j.cmpb.2020.105513.
  • Houston WJ. The analysis of errors in orthodontic measurements. Am J Orthod. 1983;83(5):382-390. DOI: 10.1016/0002-9416(83)90322-6.
  • Midtgård J, Björk G, Linder-Aronson ST. Reproducibility of cephalometric landmarks and errors of measurements of cephalometric cranial distances. Angle Orthod. 1974;44(1):56-61. DOI: 10.1043/0003-3219(1974)044<0056:ROCLAE>2.0.CO;2.
  • Eppley BL, Sadove AM. Computerized digital enhancement in craniofacial cephalometric radiography. J Oral Maxillofac Surg. 1991;49(10):1038-1043. DOI: 10.1016/0278-2391(91)90133-7.
  • Ismail WZ, Sim KS. Contrast enhancement dynamic histogram equalization for medical image processing application. Int J Imaging Syst Technol. 2011;21(3):280-289. DOI: 10.1002/ima.20295.
  • Zuiderveld K. Contrast limited adaptive histogram equalization. In: Heckbert PS, editor. Graphics Gems IV. Cambridge, MA: Academic Press; 1994.p.474-485.
  • Chung M, Lee J, Park S, Lee M, Lee CE, Lee J, Shin YG. Individual tooth detection and identification from dental panoramic x-ray images via point-wise localization and distance regularization. Artif Intell Med. 2021;111:101996. DOI: 10.1016/j.artmed.2020.101996.
  • Rashmi S, Murthy P, Ashok V, Srinath S. Cephalometric skeletal structure classification using convolutional neural networks and heatmap regression. SN Comput Sci. 2022;3(5):336. DOI: 10.1007/s42979-022-01230-w
  • Pizer SM, Amburn EP, Austin JD, Cromatie R, Geselowitz A, Greer T, Romeny BH, Zimmerman JB, Zuiderveld K. Adaptive histogram equalization and its variations. Comput Vision Graph. 1987;39(3):355-368. DOI: 10.1016/S0734-189x(87)80186-X.
  • Qassim HM, Basheer NM, Farhan MN. Brightness preserving enhancement for dental digital x-ray images based on entropy and histogram analysis. J Appl Sci Eng. 2019;22(1):187-194. DOI: 10.6180/jase.201903_22(1).0019.
  • Pandyan UM, Arumugam B, Gurunathan U, Kopuli Ashkar Ali SH. Automatic localization of inferior alveolar nerve canal in panoramic dental images. Signal Image Video P. 2022;16(5):1389-1397. DOI: 10.1007/s11760-021-02091-1.
  • Faul F, Erdfelder E, Lang AG, Buchner A. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods. 2007;39(2):175-191. DOI: 10.3758/bf03193146.
  • Koo TK, Li MY. A Guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med. 2016;15(2):155-163. DOI: 10.1016/j.jcm.2016.02.012.
  • Rossmann K, Wiley BE. The central problem in the study of radiographic image quality. Radiology. 1970;96(1):113-118. DOI: 10.1148/96.1.113.
  • McWilliam JS, Welander U. The effect of image quality on the identification of cephalometric landmarks. Angle Orthod. 1978;48(1):49-56. DOI: 10.1043/0003-3219(1978)048<0049:TEOIQO>2.0.CO;2.
  • Döler W, Steinhöfel N, Jäger A. Digital image processing techniques for cephalometric analysis. Comput Biol Med. 1991;21(1-2):23-33. DOI: 10.1016/0010-4825(91)90032-5.
  • McClure SR, Sadowsky PL, Ferreira A, Jacobson A. Reliability of digital versus conventional cephalometric radiology: A comparative evaluation of landmark identification error. Semin Orthod. 2005;11(2):98-110. DOI: 10.1053/j.sodo.2005.04.002.
  • Chen YJ, Chen SK, Yao JC, Chang HF. The effects of differences in landmark identification on the cephalometric measurements in traditional versus digitized cephalometry. Angle Orthod. 2004;74(2):155-161. DOI: 10.1043/0003-3219(2004)074<0155:TEODIL>2.0.CO;2.
  • Turner PJ, Weerakone S. An evaluation of the reproducibility of landmark identification using scanned cephalometric images. J Orthod. 2001;28(3):221-230. DOI: 10.1093/ortho/28.3.221.
  • Oshagh M, Shahidi SH, Danaei SM. Effects of image enhancement on reliability of landmark identification in digital cephalometry. Indian J Dent Res. 2013;24(1):98-103. DOI: 10.4103/0970-9290.114958.
  • Duarte H, Vieck R, Siqueira DF, Angelieri F, Bommarito S, Dalben G, Sannomiya EK. Effect of image compression of digital lateral cephalograms on the reproducibility of cephalometric points. Dentomaxillofac Radiol. 2009;38(6):393-400. DOI: 10.1259/dmfr/40996636.
  • Nikneshan S, Mohseni S, Nouri M, Hadian H, Kharazifard MJ. The effect of emboss enhancement on reliability of landmark identification in digital lateral cephalometric images. Iran J Radiol. 2015;12(2):e19302. DOI: 10.5812/iranjradiol.19302.
  • Leonardi R, Giordano D, Maiorana F, Spampinato C. Automatic cephalometric analysis. Angle Orthod. 2008;78(1):145-151. DOI: 10.2319/120506-491.1.
  • Wang CW, Huang CT, Hsieh MC, Li CH, Chang SW, Li WC, Vandaele R, Marée R, Jodogne S, Geurts P, Chen C, Zheng G, Chu C, Mirzaalian H, Hamarneh G, Vrtovec T, Ibragimov B. Evaluation and comparison of anatomical landmark detection methods for cephalometric x-ray images: A grand challenge. IEEE Trans Med Imaging. 2015;34(9):1890-1900. DOI: 10.1109/TMI.2015.2412951.
  • Wang CW, Huang CT, Lee JH, Li CH, Chang SW, Siao MJ, Lai TM, Ibragimov B, Vrtovec T, Ronneberger O, Fischer P, Cootes TF, Lindner C. A benchmark for comparison of dental radiography analysis algorithms. Med Image Anal. 2016;31:63-76. DOI: 10.1016/j.media.2016.02.004.
  • Park JH, Hwang HW, Moon JH, Yu Y, Kim H, Her SB, Srinivasan G, Aljanabi MNA, Donatelli RE, Lee SJ. Automated identification of cephalometric landmarks: Part 1-comparisons between the latest deep-learning methods YOLOV3 and SSD. Angle Orthod. 2019;89(6):903-909. DOI: 10.2319/022019-127.1.
  • Hwang HW, Park JH, Moon JH, Yu Y, Kim H, Her SB, Srinivasan G, Aljanabi MNA, Donatelli RE, Lee SJ. Automated identification of cephalometric landmarks: Part 2-might it be better than human? Angle Orthod. 2020;90(1):69-76. DOI: 10.2319/022019-129.1
  • Hwang HW, Moon JH, Kim MG, Donatelli RE, Lee SJ. Evaluation of automated cephalometric analysis based on the latest deep learning method. Angle Orthod. 2021;91(3):329-335. DOI: 10.2319/021220-100.1.
  • Kumari AR, Rao SN, Reddy PR. Design of hybrid dental caries segmentation and caries detection with meta-heuristic-based ResneXt-RNN. Biomed Signal Process Control. 2022;78:103961 DOI: 10.1016/j.bspc.2022.103961.
  • Georgieva VM, Mihaylova AD, Petrov PP. An application of dental x-ray image enhancement. In: Dimitrijević T, Stošić B, editors. 13th International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS); 2017 Oct 18-20; Nis, Serbia; 2017. pp.447-450.
  • Li W, Jiang X, Sun W, Wang SH, Liu C, Zhang X, Zhang YD, Zhou W, Miao L. Gingivitis identification via multichannel gray‐level co‐occurrence matrix and particle swarm optimization neural network. Int J Imaging Syst Technol. 2020;30(2):401-411. DOI: 10.1002/ima.22385.
  • Rahmi-Fajrin H, Puspita S, Riyadi S, Sofiani E. Dental radiography image enhancement for treatment evaluation through digital image processing. J Clin Exp Dent. 2018;10(7):e629-e634. DOI: 10.4317/jced.54607
  • Bhan A, Thakur A, Vyas G. Analysis of histogram based compound contrast enhancement with noise reduction method for endodontic therapy. 5th International Conference-Confluence The Next Generation Information Technology Summit (Confluence); 2014 Sept 25-26; Noida, India; 2014. pp.533-537.
  • Nishimoto S, Sotsuka Y, Kawai K, Ishise H, Kakibuchi M. Personal computer-based cephalometric landmark detection with deep learning, using cephalograms on the internet. J Craniofac Surg. 2019;30(1):91-95. DOI: 10.1097/SCS.0000000000004901.
  • Yao J, Zeng W, He T, Zhou S, Zhang Y, Guo J, Tang W. Automatic localization of cephalometric landmarks based on convolutional neural network. Am J Orthod Dentofacial Orthop. 2022;161(3):e250-e259. DOI: 10.1016/j.ajodo.2021.09.012.
  • Durão AP, Morosolli A, Pittayapat P, Bolstad N, Ferreira AP, Jacobs R. Cephalometric landmark variability among orthodontists and dentomaxillofacial radiologists: A comparative study. Imaging Sci Dent. 2015;45(4):213-220. DOI: 10.5624/isd.2015.45.4.213
  • Ha EG, Jeon KJ, Kim YH, Kim JY, Han SS. Automatic detection of mesiodens on panoramic radiographs using artificial intelligence. Sci Rep. 2021;11(1):23061. DOI: 10.1038/s41598-021-02571-x.
  • Menezes LD, Silva TP, Lima Dos Santos MA, Hughes MM, Mariano Souza SD, Leite Ribeiro PM, Freitas PH, Takeshita WM. Assessment of landmark detection in cephalometric radiographs with different conditions of brightness and contrast using the an artificial intelligence software. Dentomaxillofac Radiol. 2023;52(8):20230065. DOI: 10.1259/dmfr.20230065.
There are 44 citations in total.

Details

Primary Language English
Subjects Oral and Maxillofacial Radiology, Orthodontics and Dentofacial Orthopaedics
Journal Section Articles
Authors

Merve Gonca 0000-0003-1299-9088

Çiğdem Sazak 0000-0003-0600-5630

Şeyma Gündoğdu 0000-0001-5135-3007

Early Pub Date September 27, 2024
Publication Date September 30, 2024
Submission Date September 8, 2023
Published in Issue Year 2024 Volume: 14 Issue: 3

Cite

APA Gonca, M., Sazak, Ç., & Gündoğdu, Ş. (2024). Effects of Contrast Limited Adaptive Histogram Equalization (CLAHE) on Manual and Automated Tracing of Lateral Cephalometric Radiographs. Clinical and Experimental Health Sciences, 14(3), 733-744. https://doi.org/10.33808/clinexphealthsci.1357008
AMA Gonca M, Sazak Ç, Gündoğdu Ş. Effects of Contrast Limited Adaptive Histogram Equalization (CLAHE) on Manual and Automated Tracing of Lateral Cephalometric Radiographs. Clinical and Experimental Health Sciences. September 2024;14(3):733-744. doi:10.33808/clinexphealthsci.1357008
Chicago Gonca, Merve, Çiğdem Sazak, and Şeyma Gündoğdu. “Effects of Contrast Limited Adaptive Histogram Equalization (CLAHE) on Manual and Automated Tracing of Lateral Cephalometric Radiographs”. Clinical and Experimental Health Sciences 14, no. 3 (September 2024): 733-44. https://doi.org/10.33808/clinexphealthsci.1357008.
EndNote Gonca M, Sazak Ç, Gündoğdu Ş (September 1, 2024) Effects of Contrast Limited Adaptive Histogram Equalization (CLAHE) on Manual and Automated Tracing of Lateral Cephalometric Radiographs. Clinical and Experimental Health Sciences 14 3 733–744.
IEEE M. Gonca, Ç. Sazak, and Ş. Gündoğdu, “Effects of Contrast Limited Adaptive Histogram Equalization (CLAHE) on Manual and Automated Tracing of Lateral Cephalometric Radiographs”, Clinical and Experimental Health Sciences, vol. 14, no. 3, pp. 733–744, 2024, doi: 10.33808/clinexphealthsci.1357008.
ISNAD Gonca, Merve et al. “Effects of Contrast Limited Adaptive Histogram Equalization (CLAHE) on Manual and Automated Tracing of Lateral Cephalometric Radiographs”. Clinical and Experimental Health Sciences 14/3 (September 2024), 733-744. https://doi.org/10.33808/clinexphealthsci.1357008.
JAMA Gonca M, Sazak Ç, Gündoğdu Ş. Effects of Contrast Limited Adaptive Histogram Equalization (CLAHE) on Manual and Automated Tracing of Lateral Cephalometric Radiographs. Clinical and Experimental Health Sciences. 2024;14:733–744.
MLA Gonca, Merve et al. “Effects of Contrast Limited Adaptive Histogram Equalization (CLAHE) on Manual and Automated Tracing of Lateral Cephalometric Radiographs”. Clinical and Experimental Health Sciences, vol. 14, no. 3, 2024, pp. 733-44, doi:10.33808/clinexphealthsci.1357008.
Vancouver Gonca M, Sazak Ç, Gündoğdu Ş. Effects of Contrast Limited Adaptive Histogram Equalization (CLAHE) on Manual and Automated Tracing of Lateral Cephalometric Radiographs. Clinical and Experimental Health Sciences. 2024;14(3):733-44.

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