1 research outputs found
Automatic segmenting teeth in X-ray images: Trends, a novel data set, benchmarking and future perspectives
This review presents an in-depth study of the literature on segmentation
methods applied in dental imaging. Ten segmentation methods were studied and
categorized according to the type of the segmentation method (region-based,
threshold-based, cluster-based, boundary-based or watershed-based), type of
X-ray images used (intra-oral or extra-oral) and characteristics of the dataset
used to evaluate the methods in the state-of-the-art works. We found that the
literature has primarily focused on threshold-based segmentation methods (54%).
80% of the reviewed papers have used intra-oral X-ray images in their
experiments, demonstrating preference to perform segmentation on images of
already isolated parts of the teeth, rather than using extra-oral X-rays, which
show tooth structure of the mouth and bones of the face. To fill a scientific
gap in the field, a novel data set based on extra-oral X-ray images are
proposed here. A statistical comparison of the results found with the 10 image
segmentation methods over our proposed data set comprised of 1,500 images is
also carried out, providing a more comprehensive source of performance
assessment. Discussion on limitations of the methods conceived over the past
year as well as future perspectives on exploiting learning-based segmentation
methods to improve performance are also provided