  As the most economical and routine auxiliary examination in the diagnosis of
root canal treatment, oral X-ray has been widely used by stomatologists. It is
still challenging to segment the tooth root with a blurry boundary for the
traditional image segmentation method. To this end, we propose a model for
high-resolution segmentation based on polynomial curve fitting with landmark
detection (HS-PCL). It is based on detecting multiple landmarks evenly
distributed on the edge of the tooth root to fit a smooth polynomial curve as
the segmentation of the tooth root, thereby solving the problem of fuzzy edge.
In our model, a maximum number of the shortest distances algorithm (MNSDA) is
proposed to automatically reduce the negative influence of the wrong landmarks
which are detected incorrectly and deviate from the tooth root on the fitting
result. Our numerical experiments demonstrate that the proposed approach not
only reduces Hausdorff95 (HD95) by 33.9% and Average Surface Distance (ASD) by
42.1% compared with the state-of-the-art method, but it also achieves excellent
results on the minute quantity of datasets, which greatly improves the
feasibility of automatic root canal therapy evaluation by medical image
computing.
