title: Cross gender-age trabecular texture analysis in cone beam CT.
abstract: OBJECTIVE
  To investigate whether multiple texture features in different regions of interest (ROIs) on cone beam CT (CBCT) are correlated with gender-age variation of trabecular patterns.
  
  
  METHODS
  CBCT volumes from 96 subjects were used. The data set was divided into four gender-age subgroups, including males younger than 40 years, males older than 40 years, females younger than 40 years and females older than 40 years. For each volume, cubes containing trabecular patterns at four ROIs in the jaws were manually cropped. 18 distinct texture features were calculated and their correlation with gender-age variations at different ROIs was studied through t-test statistical analysis.
  
  
  RESULTS
  For the 432 test pairs with different gender-age groups at different ROIs and texture features tested, 149 of them were shown to be statistically different at the 0.05 significance level and 60 of them at the 0.001 significance level. These features can therefore capture changes in trabecular patterns and have the potential to be used for trabecular analysis. Furthermore, fractal features were found to be better than intensity features in separating different gender-age groups. Trabecular patterns in the body of the mandible were more correlated with gender-age changes than other ROIs.
  
  
  CONCLUSIONS
  Multiple texture features on CBCT were found to be correlated with the cross gender-age variation of trabecular patterns. The results support the use of CBCT for advanced trabecular analysis, including osteoporosis screening tools in the jaws.
authors.affiliation: Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA.
authors.forename: H
authors.initials: H
authors.lastname: Ling
authors.forename: X
authors.initials: X
authors.lastname: Yang
authors.forename: P
authors.initials: P
authors.lastname: Li
authors.forename: V
authors.initials: V
authors.lastname: Megalooikonomou
authors.forename: Y
authors.initials: Y
authors.lastname: Xu
authors.forename: J
authors.initials: J
authors.lastname: Yang
mesh_terms: D000328:Adult; D000367:Age Factors; D000465:Algorithms; D001851:Bone Diseases, Metabolic / Q000000981:diagnostic imaging; D054893:Cone-Beam Computed Tomography / Q000379:methods*; D005260:Female; D017709:Fractals; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D008334:Mandible / Q000033:anatomy & histology / Q000000981:diagnostic imaging*; D008437:Maxilla / Q000033:anatomy & histology / Q000000981:diagnostic imaging*; D010024:Osteoporosis / Q000000981:diagnostic imaging; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D012737:Sex Factors
keywords: computer-assisted image analysis; cone-beam computed tomography; fractals; osteoporosis; radiology