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Accurate segmentation of CBCT image is an essential step to generate three\u2010dimensional (3D) models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the poor image quality, including very low signal\u2010to\u2010noise ratio and the widespread image artifacts such as noise, beam hardening, and inhomogeneity, it is challenging to segment the CBCT images. In this paper, the authors present a new automatic segmentation method to address these problems.<\/jats:p><\/jats:sec><jats:sec><jats:title><jats:bold>Methods:<\/jats:bold><\/jats:title><jats:p>To segment CBCT images, the authors propose a new method for fully automated CBCT segmentation by using patch\u2010based sparse representation to (1) segment bony structures from the soft tissues and (2) further separate the mandible from the maxilla. Specifically, a region\u2010specific registration strategy is first proposed to warp all the atlases to the current testing subject and then a sparse\u2010based label propagation strategy is employed to estimate a patient\u2010specific atlas from all aligned atlases. Finally, the patient\u2010specific atlas is integrated into a<jats:italic>maximum a posteriori<\/jats:italic>probability\u2010based convex segmentation framework for accurate segmentation.<\/jats:p><\/jats:sec><jats:sec><jats:title><jats:bold>Results:<\/jats:bold><\/jats:title><jats:p>The proposed method has been evaluated on a dataset with 15 CBCT images. The effectiveness of the proposed region\u2010specific registration strategy and patient\u2010specific atlas has been validated by comparing with the traditional registration strategy and population\u2010based atlas. The experimental results show that the proposed method achieves the best segmentation accuracy by comparison with other state\u2010of\u2010the\u2010art segmentation methods.<\/jats:p><\/jats:sec><jats:sec><jats:title><jats:bold>Conclusions:<\/jats:bold><\/jats:title><jats:p>The authors have proposed a new CBCT segmentation method by using patch\u2010based sparse representation and convex optimization, which can achieve considerably accurate segmentation results in CBCT segmentation based on 15 patients.<\/jats:p><\/jats:sec>","DOI":"10.1118\/1.4868455","type":"journal-article","created":{"date-parts":[[2014,3,26]],"date-time":"2014-03-26T00:30:34Z","timestamp":1395793834000},"update-policy":"https:\/\/doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":74,"title":["Automated bone segmentation from dental CBCT images using patch\u2010based sparse representation and convex optimization"],"prefix":"10.1002","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8927-6772","authenticated-orcid":false,"given":"Li","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ken Chung","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaozong","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shu","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steve G. 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