Dental Segmentation in Cone‑beam Computed Tomography Images Using Watershed and Morphology Operators

Somayeh Kakehbaraei, Hadi Seyedarabi, Ali Taghavi Zenouz

Abstract


Teeth segmentation is an important task in computer‑aided procedures and clinical diagnosis. In this
paper, we propose an accurate and robust algorithm based on watershed and morphology operators
for teeth and pulp segmentation and a new approach for enamel segmentation in cone‑beam computed
tomography (CBCT) images. Proposed method consists of fve steps: acquiring appropriate CBCT
image, image enhancement, teeth segmentation using the marker‑controlled watershed (MCW),
enamel segmentation by global threshold, and fnally, utilizing the MCW for pulp segmentation.
Proposed algorithms evaluated on a dataset consisting 69 patient images. Experimental results show
a high accuracy and specifcity for teeth, enamel, and pulp segmentation. MCW algorithm and local
threshold are accurate and robust approaches to segment tooth, enamel, and pulp tissues. Methods
overcome the over‑segmentation phenomenon and artifacts reduction.


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