Automatic for Generating Landmark Mandibular Panoramic Radiography Image

Nur Nafi’iyah, Chastine Fatichah, Darlis Herumurti, Eha Renwi Astuti, Ramadhan Hardani Putra, Agus Subhan Akbar

Research output: Contribution to journalArticlepeer-review

Abstract

Forensic gender identification based on teeth is needed to identify incomplete victims, only bones and teeth. Previous studies identified gender by manually or semi-automatically measuring mandibular parameters based on landmark points. Gender identification, especially for victims of mass disasters, requires accuracy, so it takes longer, especially if there are many parameters to be measured. In addition, the observer's manual or semi-automatic measurements may give different results. This study proposes a new automatic approach to generate ten mandibular landmark points from panoramic radiographic images for gender identification. We propose a step, namely determining the centroid point of the mandibular image and using linear regression to predict ten mandibular landmark points such as the two condyle, two gonion, four ramus, and two body of the mandible. This study obtained panoramic radiographic images from the Academic Dental Hospital, Universitas Airlangga. We calculated the distance between expert mandibular landmark points and the predicted results to evaluate performance. The prediction of the landmark with the smallest average distance is the lower point of the mandibular body 1 pixel. In comparison, the predicted landmark with the most extended average length is a gonion of 10 pixels.

Original languageEnglish
Pages (from-to)584-595
Number of pages12
JournalInternational Journal of Intelligent Engineering and Systems
Volume17
Issue number1
DOIs
Publication statusPublished - 2024

Keywords

  • Automatic
  • Dental panoramic radiography
  • Gender identification
  • Linear regression
  • Mandibular landmark generate

Fingerprint

Dive into the research topics of 'Automatic for Generating Landmark Mandibular Panoramic Radiography Image'. Together they form a unique fingerprint.

Cite this