ACSDI An Approach of Clustering Stream Dental Panoramic Images
Keywords:
Image Segmentation Algorithms, Image Stream Algorithms, Panoramic Image analysis, Malocclusion.Abstract
In this paper we developed a simple computer-aided program that
could help detect and count the teeth by proposing clustering stream
tooth segmentation and counting method. The automatic solution to
segment and count the teeth from the dental panoramic X-ray images
is based on machine learning and geometrical features. We segment
and enhance the teeth region using the mask technique and check the
reliability of the result image using BHPF filter. We depend on a
collected and filtered 50 X-ray panoramic database images from the
dentist websites. MATLAB was utilized to develop proposed ACSDI
method and the experiments. The results show that our proposed
algorithm superiority existing ones in both sensitivity and specificity
which are above 70% for reliable counting and above 50% for
unreliable counting. This study provides data which can be used in
treatment planning by specialists such as orthodontists, plastic and
surgeons who have the capability to change the facial features.
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Copyright © 2025 by the authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). You may not alter or transform this work in any way without permission from the authors. Non-commercial use, distribution, and copying are permitted, provided that appropriate credit is given to the authors and Al-Hadba University.
