Noise in subtraction images made from pairs of intraoral radiographs: a comparison between four methods of geometric alignment

Dentomaxillofac Radiol. 2008 Jan;37(1):40-6. doi: 10.1259/dmfr/22185098.

Abstract

Objectives: To compare noise levels in subtraction images produced by four methods of geometric alignment.

Methods: 50 pairs of intraoral radiographs (Digora Optime; Soredex, Tuusula, Finland) were used in this study. Two programs were used to correct geometric differences: ToothVis 1.4 (TV) and DentalStudio 2.0 (DS). Three reference points were manually positioned in both programs (methods 1 and 2); four (method 3) and ten (method 4) reference points were positioned within TV in each of the 50 pairs of images. The standard deviation (SD) of the histogram defining the distribution of grey shades in the subtraction image was used as the statistical parameter for evaluation of homogeneity, i.e. the noise in the subtracted images.

Results: The mean and median shade of grey values were lower for images after geometric correction in TV (126.6\126.8, 126.9\126.8 and 126.1\126.7, for three- four- and ten-point alignment, respectively) than those performed with the positioning module of DS (128.7\127.5) (P<0.05). For the SD, the mean values were significantly lower with TV (4.6, 4.0 and 3.3 for three-, four- and ten-point alignment, respectively) than with DS (6.8). The range of SD values was the largest for four-point alignment with TV (0.7-15.4), smaller for three-point alignment with DS (1.5-15.4) and three-point alignment with TV (0.5-13), and the smallest with ten-point alignment in TV (0.5-8.7).

Conclusions: The SD of the grey-shade histogram showed that subtraction images produced with ToothVis 1.4 software were statistically less noisy than images produced with Dental Studio 2.0 software. There is a relationship between the number of reference points chosen and the noise in the subtraction images.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Humans
  • Radiographic Image Enhancement / methods*
  • Statistics, Nonparametric
  • Subtraction Technique*
  • Tooth / diagnostic imaging*