Ching Wei, Wang; National Taiwan University of Science and Technology > Graduate institute of BIomedical Engineering > Medical Image R&D Centre
Cheng-Ta, Huang; National Taiwan University of Science and Technology
Meng-Che, Hsieh; Simon Fraser University - SFU > Computing Science
Chu-Hsing, Li; Simon Fraser University - SFU > Computing Science
Vandaele, Rémy ; Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique
JODOGNE, Sébastien ; Centre Hospitalier Universitaire de Liège - CHU > Département de Physique Médicale
Geurts, Pierre ; Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique
Chengwen, Chu; University of Bern
Hengameh, Mirzaalian; Simon Fraser University - SFU > Computing Science
Deberry-Borowiecki, B., Kukwa, A., Blanks, R.H.I., Cephalometric analysis for diagnosis and treatment of obstructive sleep apnea (1988) Laryngoscope, 998 (2), pp. 226-234
Guilleminault, C., Quera-Salva, M.A., Partinen, M., Jamieson, A., Women and the obstructive sleep apnea syndrome (1988) CHEST, 93 (1), pp. 104-109
Hochban, W., Brandenburg, U., Morphology of the viscerocranium in obstructive sleep apnoea syndrome-cephalometric evaluation of 400 patients (1994) J. Cranio-Maxillo-Facial Surg., 22 (4), pp. 205-213
Nanda, R.S., Merrill, R.M., Cephalometric assessment of sagittal relationship between maxilla and mandible (1994) Am. J. Orthodontics Dentofacial Orthopedics, 105 (4), pp. 328-344
Lake, S.L., McNeill, R.W., Little, R.M., West, R.A., Surgical mandibular advancement: A cephalometric analysis of treatment response (1981) Am. J. Orthodontics, 80 (4), pp. 376-394
Melnik, A.K., A cephalometric study of mandibular asymmetry in a longitudinally followed sample of growing children (1992) Am. J. Orthodontics Dentofacial Orthopedics, 101 (4), pp. 355-366
Hajeer, M.Y., Ayoub, A.F., Millett, D.T., Three-dimensional assessment of facial soft-tissue asymmetry before and after orthognathic surgery (2004) Br. J. Oral Maxillofacial Surg., 42 (5), pp. 396-404
McDonnell, J.P., McNeill, R.W., West, R.A., Advancement genioplasty: A retrospective cephalometric analysis of osseous and soft tissue changes (1977) J. Oral Surg., 35 (8), pp. 640-647
De Oliveira, A.E., Observer reliability of three-dimensional cephalometric landmark identification on cone-beam computerized tomography (2009) Oral Surg., Oral Med., Oral Pathol., Oral Radiol., 107 (2), pp. 256-265
Major, P.W., Johnson, D.E., Hesse, K.L., Glover, K.E., Effect of head orientation on posterior anterior cephalometric landmark identification (1996) Angle Orthodontist, 66 (1), pp. 51-60
Chen, Y.J., Chen, S.K., Chang, H.F., Chen, K.C., Comparison of landmark identification in traditional versus computer-aided digital cephalometry (2000) Angle Orthodontist, 70 (5), pp. 387-392
Rakosi, T., (1982) An Atlas and Manual of Cephalometric Radiology, , London, U. K. : Wolfe Medical
El-Feghi, I., Sid-Ahmed, M.A., Ahmadi, M., Automatic localization of craniofacial landmarks for assisted cephalometry (2004) Pattern Recognit., 37 (3), pp. 609-621
Kamoen, A., Dermaut, L., Verbeeck, R., The clinical significance of error measurement in the interpretation of treatment results (2001) Eur. J. Orthodontics, 23 (5), pp. 569-578
Kaur, A., Singh, C., Cephalometric x-ray registration using angular radial transform (2012) IJCA Proc. Int. Conf. Recent Advances Future Trends Inf. Technol., pp. 18-22
McClure, S.R., Sadowsky, P.L., Ferreira, A., Jacobson, A., Reliability of digital versus conventional cephalometric radiology: A comparative evaluation of landmark identification error (2005) Sem. Orthodontics, 11 (2), pp. 98-110
Delamare, E.L., Influence of a programme of professional calibration in the variability of landmark identification using cone beam computed tomography-synthesized and conventional radiographic cephalograms (2010) Dentomaxillofac Radiol., 39 (7), pp. 414-423
Yue, W., Yin, D., Li, C., Wang, G., Xu, T., Automated 2-D cephalometric analysis on x-ray images by a model-based approach (2006) IEEE Trans. Biomed. Eng., 53 (8), pp. 1615-1623. , Aug
Kafieh, R., Sadri, S., Mehri, A., Raji, H., Discrimination of bony structures in cephalograms for automatic landmark detection (2009) Commun. Comput. Inf. Sci., 6 (1), pp. 609-620
Kaur, A., Singh, C., Automatic cephalometric landmark detection using zernike moments and template matching (2015) Signal, Image Video Process., 9 (1), pp. 117-132
Ferreira, J.T.L., Telles, C.D.S., Evaluation of the reliability of computerized profile cephalometric analysis (2002) Brazilian Dental J., 13 (3), pp. 201-204
Wang, C.W., Huang, C.T., Li, C.H., Chang, S.W., A grand challenge for automated detection of critical landmarks for cephalometric x-ray image analysis IEEE Int. Symp. Biomed. Imag
Vucinic, P., Trpovski, Z., Scepan, I., Automatic landmarking of cephalograms using active appearance models (2010) Eur. J. Orthodontics, 32 (1), pp. 233-241
Ibragimov, B., Likar, B., Pernuš, F., Vrtovec, T., Shape representation for efficient landmark-based segmentation in 3D (2014) IEEE Trans. Med. Imag., 33 (4), pp. 861-874. , Apr
Ibragimov, B., Likar, B., Pernuš, F., Vrtovec, T., A game-theoretic framework for landmark-based image segmentation (2012) IEEE Trans. Med. Imag., 31 (9), pp. 1761-1776. , Sep
Ibragimov, B., Likar, B., Pernuš, F., Vrtovec, T., Automatic cephalometric X-ray landmark detection by applying game theory and random forests (2014) Proc. ISBI Int. Symp. Biomed. Imag. 2014, pp. 1-8. , Automat. Cephalometric X-Ray Landmark Detection Challenge, Beijing, China
Chu, C., Chen, C., Nolte, L.-P., Zheng, G., Fully automatic cephalometric x-ray landmark detection using random forest regression and sparse shape composition (2014) Proc. ISBI Int. Symp. Biomed. Imag. 2014, pp. 9-16. , Automat. Cephalometric X-Ray Landmark Detection Challenge, Beijing, China
Chen, C., Zheng, G., Fully-automatic landmark detection in cephalometric x-ray images by data-driven image displacement estimation (2014) Proc. ISBI Int. Symp. Biomed. Imag. 2014, pp. 17-24. , Automat. Cephalometric X-Ray Landmark Detection Challenge, Beijing, China
Mirzaalian, H., Hamarneh, G., Automatic globally-optimal pictorial structures with random decision forest based likelihoods for cephalometric x-ray landmark detection (2014) Proc. ISBI Int. Symp. Biomed. Imag. 2014, pp. 25-36. , Automat. Cephalometric X-Ray Landmark Detection Challenge, Beijing, China
Vandaele, R., Marée, R., Jodogne, S., Geurts, P., Automatic cephalometric x-ray landmark detection challenge 2014: A tree-based approach (2014) Proc. ISBI Int. Symp. Biomed. Imag. 2014, pp. 37-44. , Automat. Cephalometric X-Ray Landmark Detection Challenge, Beijing, China
Breiman, L., Random forests (2001) Mach. Learn., 45 (1), pp. 5-32
Criminisi, A., Shotton, J., Robertson, D., Konukoglu, E., Regression forests for efficient anatomy detection and localization in CT studies (2011) Proc. MICCAI 2010 Int. Conf. Med. Image Comput. Comput. Assist. Intervent., LNCS, 6533 (1), pp. 106-117
Lindner, C., Fully automatic segmentation of the proximal femur using random forest regression voting (2013) IEEE Trans. Med. Imag., 32 (8), pp. 1462-1472. , Aug
Zhang, S., Sparse shape composition: A new framework for shape prior modeling (2011) Proc. CVPR IEEE Conf. Comput. Vis. Pattern Recognit., pp. 1025-1032
Chen, C., Automatic X-ray landmark detection and shape segmentation via data-driven joint estimation of image displacements (2014) Med. Image Anal., 18 (3), pp. 487-499
Ojala, T., Pietikinen, M., Harwood, D., Performance evaluation of texture measures with classification based on Kullback discrimination of distributions (1994) Proc. ICPR Int. Conf. Pattern Recognit., 1, pp. 582-585
Frangi, A., Niessen, W.J., Vincken, L., Viergever, A., Multiscale vessel enhancement filtering (1998) Proc. MICCAI 1998 Int. Conf. Med. Image Comput. Comput. Assist. Intervent., 1, pp. 130-137
Felzenszwalb, P., Huttenlocher, D., Pictorial structures for object recognition (2005) Int. J. Comput. Vis., 61 (1), pp. 55-79
Geurts, P., Ernst, D., Wehenkel, L., Extremely randomized trees (2006) Mach. Learn., 63 (1), pp. 3-42
Pedregosa, F., Scikit-learn: Machine learning in Python (2011) J. Mach. Learn. Res., 12, pp. 2825-2830
Stern, O., Automatic localization of interest points in zebrafish images with tree-based methods (2011) Pattern Recognit. Bioinformat., 7036 (1), pp. 179-190
Seres, L., Correction of a severe facial asymmetry with computerized planning and with the use of a rapid prototyped surgical template: A case report/technique article (2014) Head FaceMed., 10 (27)
Kragskov, J., Bosch, C., Gyldensted, C., Sindet-Pedersen, S., Comparison of the reliability of craniofacial anatomic landmarks based on cephalometric radiographs and three-dimensional CT scans (1997) Cleft Palate-Craniofacial J., 34 (2), pp. 111-116
Gliddon, M.J., The accuracy of cephalometric tracing superimposition (2006) J. Oral Maxillofacial Surg., 64 (2), pp. 194-202
Kwon, T.G., Park, H.S., Ryoo, H.M., Lee, S.H., A comparison of craniofacial morphology in patients with and without facial asymmetry-a three-dimensional analysis with computed tomography (2006) J. Oral Maxillofacial Surg., 35 (1), pp. 43-48
Bholsithi, W., Tharanon, W., Chintakanon, K., Komolpis, R., Sinthanayothin, C., 3D vs. 2D cephalometric analysis comparisons with repeated measurements from 20 Thai males and 20 Thai females (2009) Biomed. Imag. Intervent. J., 5 (4)
Pereira, A.L., De-Marchi, L.M., Scheibel, P.C., Ramos, A.L., Reproducibility of natural head position in profile photographs of children aged 8 to 12 years with and without the aid of a cephalostat (2010) Dental Press J. Orthodont., 15 (1), pp. 65-73
