Study of edge detection task in dental panoramic radiographs

. 2013 ; 42 (7) : 20120391. [epub] 20130502

Jazyk angličtina Země Anglie, Velká Británie Médium print-electronic

Typ dokumentu srovnávací studie, časopisecké články, práce podpořená grantem

Perzistentní odkaz   https://www.medvik.cz/link/pmid23640989

The purpose of this study is (1) to introduce a new approach for edge detection in orthopantograms (OPGs) and an improved automatic parameter selector for common edge detectors, (2) to present a comparison between our novel approach with common edge detectors and (3) to provide faster outputs without compromising quality. A new approach for edge detection based on statistical measures was introduced: (1) a set of N edge detection results is calculated from a given input image and a selected type of edge detector, (2) N correspondence maps are constructed from N edge detection results, (3) probabilities and average probabilities are computed, (4) an overall correspondence is evaluated for each correspondence map and (5) the correspondence map providing the best overall correspondence is taken as the result of edge detection procedure. A comparison with common edge detectors (the Roberts, Prewitt, Sobel, Laplacian of the Gaussian and Canny methods) with various parameter settings (304 combinations for each test image) was carried out. The methods were assessed objectively [edge mismatch error (EME), modified Hausdorff distance (MHD) and principal component analysis] and subjectively by experts in dentistry and based on time demands. The suitability of the new approach for edge detection in OPGs was confirmed by experts. The current conventional methods in edge detection in OPGs are inadequate (none of the tested methods reach an EME value or MHD value below 0.1). Our proposed approach for edge detection shows promising potential for its implementation in clinical dentistry. It enhances the accuracy of OPG interpretation and advances diagnosis and treatment planning.

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Dhawan AP. Medical image analysis. 2nd edn Hoboken, NJ: John Wiley & Sons; 2011

Roberts LG. Machine perception of three dimensional solids. PhD thesis. Cambridge, MA: Massachusetts Institute of Technology, Electrical Engineering Department; 1963

Prewitt JMS. Object enhancement and extraction. In: Rosenfeld A, Lipkin BS. (eds). Picture processing and psychophysics. New York, NY: Academic Press; 1970. pp 75–149

Pingle KK. Visual perception by computer. In: Grasselli A. (ed.). Automatic interpretation and classification of images. New York, NY: Academic Press; 1969. pp 277–284

Robinson G. Edge detection by compass gradient masks. Comput Graphics Image Process 1977; 6: 492–501

Marr D. Vision. New York, NY: Freeman Publishers; 1982

Canny JA. Computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 1986; 8: 679–698 PubMed

Muramatsu C, Matsumoto T, Hayashi T, Hara T, Katsumata A, Zhou X, et al. Automated measurement of mandibular cortical width on dental panoramic radiographs. Int J Comput Assist Radiol Surg. 2012 doi: 10.1007/s11548-012-0800-8. Nov . . Epub ahead of print. PubMed DOI

Sezgin M, Sankur B. Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging 2004; 13: 146–165

Yitzhaky Y, Peli E. A method for objective edge detection evaluation and detector parameter selection. IEEE Trans Pattern Anal Mach Intell 2003; 25: 1027–1033

Koren R, Yitzhaky Y. Automatic selection of edge detector parameters based on spatial and statistical measures. Comput Vis Image Underst 2006; 102: 204–213

Medina-Carnicer R, Munoz-Salinas R, Yeguas-Bolivar E, Diaz-Mas L. A novel method to look for the hysteresis thresholds for the Canny edge detector. Pattern Recogn 2011; 44: 1201–1211

López-López J, Álvarez-López JM, Jané-Salas E, Estrugo-Devesa A, Ayuso-Montero R, Velasco-Ortega E, et al. Computer-aided system for morphometric mandibular index computation (using dental panoramic radiographs). Med Oral Patol Oral Cir Bucal 2012; 17: e624–e632 PubMed PMC

Chen H, Jain AK. Dental biometrics: alignment and matching of dental radiographs. IEEE Trans Pattern Anal Mach Intell 2005; 27: 1319–1326 10.1109/TPAMI.2005.157 PubMed DOI

Samaras CD. Digital radiography: the standard of care. Compend Contin Educ Dent 2008; 29: 506, 508–509 PubMed

Kamburoglu K, Kolsuz E, Murat S, Yüksel S, Ozen T. Proximal caries detection accuracy using intraoral bitewing radiography, extraoral bitewing radiography and panoramic radiography. Dentomaxillofac Radiol 2012; 41: 450–459 10.1259/dmfr/30526171 PubMed DOI PMC

Langland OE, Langlais RP, Preece JW. Principles of dental imaging. 2nd edn Baltimore, MD: Lippincott Williams and Wilkins; 2002

Sanderink GC, Visser WN, Kramers EW. The origin of a case of severe image distortion in rotational panoramic radiography. Dentomaxillofac Radiol 1991; 20: 169–171 PubMed

Choi YG, Kim YK, Eckert SE, Shim CH. Cross-sectional study of the factors that influence radiographic magnification of implant diameter and length. Int J Oral Maxillofac Implants 2004; 19: 594–596 PubMed

Samawi SS, Burke PH. Angular distortion in the orthopantomogram. Br J Orthod 1984; 11: 100–107 PubMed

Devlin H, Yuan J. Object position and image magnification in dental panoramic radiography: a theoretical analysis. Dentomaxillofac Radiol 2013; 42: 29951683 10.1259/dmfr/29951683 PubMed DOI PMC

van der Stelt PF. Better imaging: the advantages of digital radiography. J Am Dent Assoc 2008; 139: 7S–13S PubMed

Gormez O, Yilmaz HH. Image post-processing in dental practice. Eur J Dent 2009; 3: 343–347 PubMed PMC

Nakamoto T, Taguchi A, Ohtsuka M, Suei Y, Fujita M, Tsuda M, et al. A computer-aided diagnosis system to screen for osteoporosis using dental panoramic radiographs. Dentomaxillofac Rad 2008; 37: 274–281 10.1259/dmfr/68621207 PubMed DOI

Nomir O, Abdel-Mottaleb M. Fusion of matching algorithms for human identification using dental X-ray radiographs. IEEE Trans Inf Forensics Security 2008; 3: 223–233

Lim JS. Two-dimensional signal and image processing. Englewood Cliffs, NJ: Prentice Hall; 1990. pp 478–488

Parker JR. Algorithms for image processing and computer vision. New York, NY: John Wiley & Sons, Inc.; 1997. pp 23–29

Zhang YJ. A survey on evaluation methods for image segmentation. Pattern Recogn 1996; 29: 1335–1346

Yasnoff WA, Mui JK, Bacus JW. Error measures for scene segmentation. Pattern Recogn 1977; 9: 217–231

Sezgin M, Sankur B. Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging 2004; 13: 146–165

Jolliffe IT. Principal component analysis. New York, NY: Springer; 1986

Bennemann R, Baxmann M, Keilig L, Reimann S, Braumann B, Bourauel C. Evaluating miniscrew position using orthopantomograms compared to cone-beam computed tomography. J Orofac Orthop 2012; 73: 236–248 10.1007/s00056-012-0079-y PubMed DOI

Troeltzsch M, Liedtke J, Troeltzsch V, Frankenberger R, Steiner T, Troeltzsch M. Odontoma-associated tooth impaction: accurate diagnosis with simple methods? Case report and literature review. J Oral Maxillofac Surg 2012; 70: e516–e520 10.1016/j.joms.2012.05.030 PubMed DOI

Jacobs R. Dental cone beam CT and its justified use in oral health care. JBR-BTR. 2011; 94: 254–265 PubMed

Kraemer HC. Evaluating medical tests: objective and quantitative guidelines. Newbury Park, CA: Sage Publications; 1992

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