Please use this identifier to cite or link to this item: https://doi.org/10.1007/s11548-012-0806-2
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dc.titleA two-stage rule-constrained seedless region growing approach for mandibular body segmentation in MRI
dc.contributor.authorJi, D.X.
dc.contributor.authorFoong, K.W.C.
dc.contributor.authorOng, S.H.
dc.date.accessioned2014-06-17T02:36:06Z
dc.date.available2014-06-17T02:36:06Z
dc.date.issued2013-09
dc.identifier.citationJi, D.X., Foong, K.W.C., Ong, S.H. (2013-09). A two-stage rule-constrained seedless region growing approach for mandibular body segmentation in MRI. International Journal of Computer Assisted Radiology and Surgery 8 (5) : 723-732. ScholarBank@NUS Repository. https://doi.org/10.1007/s11548-012-0806-2
dc.identifier.issn18616410
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/54837
dc.description.abstractPurpose Extraction of the mandible from 3D volumetric images is frequently required for surgical planning and evaluation. Image segmentation from MRI is more complex than CT due to lower bony signal-to-noise. An automated method to extract the human mandible body shape from magnetic resonance (MR) images of the head was developed and tested. Methods Anonymous MR images data sets of the head from 12 subjects were subjected to a two-stage rule-constrained region growing approach to derive the shape of the body of the human mandible. An initial thresholding technique was applied followed by a 3D seedless region growing algorithm to detect a large portion of the trabecular bone (TB) regions of the mandible. This stage is followed with a rule-constrained 2D segmentation of each MR axial slice to merge the remaining portions of the TB regions with lower intensity levels. The two-stage approach was replicated to detect the cortical bone (CB) regions of the mandibular body. The TB and CB regions detected from the preceding steps were merged and subjected to a series of morphological processes for completion of the mandibular body region definition. Comparisons of the accuracy of segmentation between the two-stage approach, conventional region growing method, 3D level set method, and manual segmentation were made with Jaccard index, Dice index, and mean surface distance (MSD). Results The mean accuracy of the proposed method is 0.958 ± 0.020 for Jaccard index, 0.979 ±0.011 for Dice index, and 0.204 ±0.127 mm for MSD. The mean accuracy of CRG is 0.782 ±0.080 for Jaccard index, 0.876 ±0.053 for Dice index, and 0.417 ±0.073 mm for MSD. The mean accuracy of the 3D level set method is 0.874 ±0.0.051 for Jaccard index, 0.645±0.306 for Dice index, and 0.645±0.306 mm for MSD. The proposed method shows improvement in accuracy over CRG and 3D level set. Conclusion Accurate segmentation of the body of the human mandible from MR images is achieved with the proposed two-stage rule-constrained seedless region growing approach. The accuracy achieved with the two-stage approach is higher than CRG and 3D level set. © 2013 CARS.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/s11548-012-0806-2
dc.sourceScopus
dc.subject2D region growing
dc.subject3D region growing
dc.subjectMandible
dc.subjectMRI
dc.subjectSegmentation
dc.typeArticle
dc.contributor.departmentDENTISTRY
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1007/s11548-012-0806-2
dc.description.sourcetitleInternational Journal of Computer Assisted Radiology and Surgery
dc.description.volume8
dc.description.issue5
dc.description.page723-732
dc.identifier.isiut000324253300004
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