A two-stage rule-constrained seedless region growing approach for mandibular body segmentation in MRI

Abstract

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 ± 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.