Comparison of 5 protocols based on their abilities to use data extracted from digitized clinical radiographs to discriminate between patients with gingivitis and periodontitis.
BACKGROUND
This study was undertaken to compare 5 digital analytic protocols for their abilities to extract data from digital clinical radiographs and discriminate between patients with gingivitis and periodontitis.


METHODS
Five digital-image analysis protocols were compared for their abilities to discriminate between two groups of 24 patients each. One group was diagnosed with healthy gingiva (or gingivitis) and the second with periodontitis. These groups were previously evaluated in published studies that used fractal and morphologic analyses. Pre-existing clinical radiographs for each patient were digitized and regions of interest (ROIs) were placed on interdental bone in mandibular posterior quadrants. The 5 protocols used were: 1) MGB: a median filtration to remove high-frequency noise, a Gaussian filtration to remove low-frequency noise, binarization of the resulting image, and quantification of the black pixels; 2) MGBS: the same protocol as MGB except for a skeletonization of the binary image and a quantification of the skeleton's pixels; 3) GBS: Gaussian filtration, binarization (thresholding on the mean pixel value) of the resulting image, skeletonization, and quantification of the pixels of the skeleton; 4) NS: normalization, skeletonization, and quantification of the skeleton's pixels; and 5) S: a variation of NS, except normalization was not used. The resulting values for the 2 patient groups were compared with Mann-Whitney U tests and effect likelihood-ratio test.


RESULTS
For digitized radiographs, the mean gray-scale value (+/- standard deviation) for gingivitis patients was 183.22 +/- 18.53 and for periodontitis patients 181.26 +/- 17.20. Mann-Whitney U tests resulted in the following P values for these protocols: MGBS <0.01; S <0.01; GBS <0.01; NS <0.01; and MGB <0.83. Effect likelihood-ratio tests indicated that only MGBS and S significantly contributed to models containing the other factors.


CONCLUSIONS
Small variations to protocols affected the strength of the discrimination between the gingivitis and periodontitis groups. While there is potential for morphologic analysis to be used to discriminate between patients with gingivitis and periodontitis, a robust technique was not identified.
Shrout
M K
MK
Department of Oral Diagnosis and Patient Services, School of Dentistry, Medical College of Georgia, Augusta 30912-1241, USA. mshrout@mail.mcg.edu
Hildebolt
C F
CF
Potter
B J
BJ
Comer
R W
RW
D000328:Adult; D000539:Alveolar Process / Q000000981:diagnostic imaging; D016477:Artifacts; D003937:Diagnosis, Differential; D005260:Female; D005374:Filtration / Q000295:instrumentation; D005891:Gingivitis / Q000000981:diagnostic imaging*; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D010518:Periodontitis / Q000000981:diagnostic imaging*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011861:Radiography, Dental / Q000379:methods*; D018709:Statistics, Nonparametric