naa a22 4500 510808123 CHVBK 20180411083427.0 cr unu---uuuuu 180411e20130901xx s 000 0 eng 10.1007/s11548-012-0806-2 doi (NATIONALLICENCE)springer-10.1007/s11548-012-0806-2 A two-stage rule-constrained seedless region growing approach for mandibular body segmentation in MRI [Elektronische Daten] [Dong Ji, Kelvin Foong, Sim Ong] Purpose 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 $$0.958 \,\pm \, 0.020$$ for Jaccard index, $$0.979 \,\pm \, 0.011$$ for Dice index, and $$0.204 \,\pm \, 0.127$$ mm for MSD. The mean accuracy of CRG is $$0.782 \,\pm \, 0.080$$ for Jaccard index, $$0.876 \,\pm \, 0.053$$ for Dice index, and $$0.417 \,\pm \, 0.073$$ mm for MSD. The mean accuracy of the 3D level set method is $$0.874 \,\pm \, 0.0.051$$ for Jaccard index, $$0.645 \pm 0.306$$ for Dice index, and $$0.645 \pm 0.306$$ 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. CARS, 2013 Mandible nationallicence Segmentation nationallicence MRI nationallicence 2D region growing nationallicence 3D region growing nationallicence Ji Dong NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore aut Foong Kelvin NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore aut Ong Sim NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore aut International Journal of Computer Assisted Radiology and Surgery Springer Berlin Heidelberg 8/5(2013-09-01), 723-732 1861-6410 8:5<723 2013 8 11548 https://doi.org/10.1007/s11548-012-0806-2 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11548-012-0806-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Ji Dong NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore aut NATIONALLICENCE 700 1- Foong Kelvin NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore aut NATIONALLICENCE 700 1- Ong Sim NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore aut NATIONALLICENCE 773 0- International Journal of Computer Assisted Radiology and Surgery Springer Berlin Heidelberg 8/5(2013-09-01), 723-732 1861-6410 8:5<723 2013 8 11548 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer