ORIGINAL ARTICLE   

Gazzetta Medica Italiana - Archivio per le Scienze Mediche 2019 May;178(5):323-6

DOI: 10.23736/S0393-3660.18.03813-5

Copyright © 2018 EDIZIONI MINERVA MEDICA

lingua: Inglese

Detection of mandibular fractures using particle swarm optimization algorithm

Somaye ANSARI MOGHADAM ¹, Ali BARZEGAR ², Nasim DAVTALAB BEHNAM ³, Mohammad NAEBI ⁴, Ayda TUPAL ³, Milad ZOKAEI ³, Sirous RISBAF FAKOUR ⁵ ✉

¹ Department of Periodontology, Zahedan Dental School, Oral and Dental Disease Research Center, Zahedan University of Medical Sciences, Zahedan, Iran; ² Department of Prosthodontics, Dental and Periodental Research Center, Tabriz University of Medical Science, Tabriz, Iran; ³ School of Dentistry, Tabriz University of Medical Science, Tabriz, Iran; ⁴ School of Dentistry, Zahedan University of Medical Science, Zahedan, Iran; ⁵ Department of Oral and Maxillofacial Surgery, Oral and Dental Disease Research Center, Zahedan University of Medical Science, Zahedan, Iran

BACKGROUND: In this study, we want to automatically diagnose mandibular fractures using a diagnostic algorithm without using direct intervention. Using a smart system in detection of the mandibular fracture, we have responded to this challenge, in this work. The purpose of this paper is detection of the mandibular fracture with processing image using particle swarm optimization (PSO) algorithm in the panoramic images that facilitate conducting a more accurate diagnosis.
METHODS: Particle swarm optimization, in principle, is a computing evolutionary technique and an optimization population-based method. This algorithm is based on examination of the color changes around the fracture in the panoramic images. The color of the fracture around bone is darker (lucent) compared with that of the bone (opaque). Methodology of this algorithm on panoramic image is to investigate the color changes around bone and to show mandibular fracture. The difference between this study and previous ones is computation of the color changes by image processing algorithm for diagnosis of the mandibular fracture.
RESULTS: After running the algorithm, if the fracture around bone, PSO algorithm can recognize mandibular fracture and identify its location.
CONCLUSIONS: This algorithm provides useful and successful results for the presented tests and experiments. Using this algorithm, it is possible to save time, reduce errors, and have a more accurate diagnosis. Among the potential applications of this algorithm is to intelligently help dentist robots.

KEY WORDS: Algorithms - Mandibular fractures - Computer-assisted image processing