3DBODY.TECH 2017 - Paper 17.192

O. Ahmad et al., "Torso Shape Extraction from 3D Body Scanning Data Using Automatic Segmentation Tool", in Proc. of 3DBODY.TECH 2017 - 8th Int. Conf. and Exh. on 3D Body Scanning and Processing Technologies, Montreal QC, Canada, 11-12 Oct. 2017, pp. 192-199, https://doi.org/10.15221/17.192.


Torso Shape Extraction from 3D Body Scanning Data Using Automatic Segmentation Tool


Ola AHMAD 1,2, Philippe DEBANNÉ 1,2, Stefan PARENT 2, Hubert LABELLE 2, Farida CHERIET 1,2

1 Polytechnique Montréal, Montreal QC, Canada;
2 CHU Sainte-Justine, Montreal QC, Canada


The automatic and standardized extraction of the torso shape from 3D body scanning data has an important role in biomedical applications. In scoliosis clinics, the asymmetry analysis of the 3D scoliotic trunk shape relies on a prior cropping of the regions corresponding to the arms and neck. At Sainte-Justine Hospital, a system of four optical digitizers (Capturor II LF, Creaform Inc.) is used to scan the body of scoliosis patients. At present, the cropping of the trunk shapes is a manual process and is therefore operator-dependent, time-consuming and can affect the reliability of subsequent trunk asymmetry analysis. In addition, the inferior portion of the trunk (pelvic region) includes noisy geometric features that are due to the patient's lower body clothing and are irrelevant to the study of scoliotic trunk shape deformations. In this paper, we present a robust and efficient tool to extract the meaningful torso regions based on automatic segmentation. The 3D body scanning system provides a 3D triangulated mesh of the shape accompanied by an RGB color map of the texture. An anatomical landmark placed at the midpoint of the posterior-anterior iliac spines (MPSIS) prior to the acquisition determines the separation level between the pelvic region and the rest of the torso (i.e. the lumbar and thoracic regions). We propose a two-phase segmentation algorithm. In the first phase, a skin-color model is used to separate the pelvic region from the other portions of the torso. The second phase separates the arms and neck regions using relevant geometric features captured by a spectral representation of the shape. We tested our algorithm on a dataset composed of 56 scoliotic body shapes scanned in neutral standing and lateral bending postures by comparing the torsos cropped automatically versus manually by an operator. The results show that our algorithm achieves a 0.95 (+-0.04) degree of overlap, in terms of the average Dice similarity measure, between the extracted torso shapes and their ground truth counterparts. The proposed automatic segmentation method thus constitutes a useful tool to include in the 3D body surface scanning systems used in scoliosis clinics.


Full paper: 17.192.pdf
Proceedings: 3DBODY.TECH 2017, 11-12 Oct. 2017, Montreal QC, Canada
Pages: 192-199
DOI: 10.15221/17.192

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