Literature DB >> 26587551

Automatic segmentation of mandible in panoramic x-ray.

Amir Hossein Abdi1, Shohreh Kasaei1, Mojdeh Mehdizadeh2.   

Abstract

As the panoramic x-ray is the most common extraoral radiography in dentistry, segmentation of its anatomical structures facilitates diagnosis and registration of dental records. This study presents a fast and accurate method for automatic segmentation of mandible in panoramic x-rays. In the proposed four-step algorithm, a superior border is extracted through horizontal integral projections. A modified Canny edge detector accompanied by morphological operators extracts the inferior border of the mandible body. The exterior borders of ramuses are extracted through a contour tracing method based on the average model of mandible. The best-matched template is fetched from the atlas of mandibles to complete the contour of left and right processes. The algorithm was tested on a set of 95 panoramic x-rays. Evaluating the results against manual segmentations of three expert dentists showed that the method is robust. It achieved an average performance of [Formula: see text] in Dice similarity, specificity, and sensitivity.

Entities:  

Keywords:  automatic segmentation; mandible; medical image; panoramic x-ray; statistical modeling

Year:  2015        PMID: 26587551      PMCID: PMC4652330          DOI: 10.1117/1.JMI.2.4.044003

Source DB:  PubMed          Journal:  J Med Imaging (Bellingham)        ISSN: 2329-4302


  6 in total

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2.  Unified segmentation.

Authors:  John Ashburner; Karl J Friston
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Journal:  Med Image Comput Comput Assist Interv       Date:  2006

4.  A Bayesian model for joint segmentation and registration.

Authors:  Kilian M Pohl; John Fisher; W Eric L Grimson; Ron Kikinis; William M Wells
Journal:  Neuroimage       Date:  2006-02-07       Impact factor: 6.556

5.  A generative model for image segmentation based on label fusion.

Authors:  Mert R Sabuncu; B T Thomas Yeo; Koen Van Leemput; Bruce Fischl; Polina Golland
Journal:  IEEE Trans Med Imaging       Date:  2010-06-17       Impact factor: 10.048

6.  Automatic craniofacial structure detection on cephalometric images.

Authors:  Tanmoy Mondal; Ashish Jain; H K Sardana
Journal:  IEEE Trans Image Process       Date:  2011-03-24       Impact factor: 10.856

  6 in total
  2 in total

1.  A Novel Registration-Based Semiautomatic Mandible Segmentation Pipeline Using Computed Tomography Images to Study Mandibular Development.

Authors:  Ying Ji Chuang; Benjamin M Doherty; Nagesh Adluru; Moo K Chung; Houri K Vorperian
Journal:  J Comput Assist Tomogr       Date:  2018 Mar/Apr       Impact factor: 1.826

2.  Automated description of the mandible shape by deep learning.

Authors:  Nicolás Vila-Blanco; Paulina Varas-Quintana; Ángela Aneiros-Ardao; Inmaculada Tomás; María J Carreira
Journal:  Int J Comput Assist Radiol Surg       Date:  2021-08-27       Impact factor: 2.924

  2 in total

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