Strut analysis for osteoporosis detection model using dental panoramic radiography

Abstract

Objectives: The aim of this study was to identify variables that can be used for osteoporosis detection using strut analysis, fractal dimension (FD) and the gray level co-occurrence matrix (GLCM) using multiple regions of interest and to develop an osteoporosis detection model based on panoramic radiography.

Methods: A total of 454 panoramic radiographs from oral examinations in our dental hospital from 2012 to 2015 were randomly selected, equally distributed among osteoporotic and non-osteoporotic patients (n = 227 in each group). The radiographs were classified by bone mineral density (T-score). After 3 marrow regions and the endosteal margin area were selected, strut features, FD and GLCM were analysed using a customized image processing program. Image upsampling was used to obtain the optimal binarization for calculating strut features and FD. The independent-samples t-test was used to assess statistical differences between the 2 groups. A decision tree and support vector machine were used to create and verify an osteoporosis detection model.

Results: The endosteal margin area showed statistically significant differences in FD, GLCM and strut variables between the osteoporotic and non-osteoporotic patients, whereas the medullary portions showed few distinguishing features. The sensitivity, specificity, and accuracy of the strut variables in the endosteal margin area were 97.1%, 95.7 and 96.25 using the decision tree and 97.2%, 97.1 and 96.9% using support vector machine, and these were the best results obtained among the 3 methods. Strut variables with FD and/or GLCM did not increase the diagnostic accuracy.

Conclusion: The analysis of strut features in the endosteal margin area showed potential for the development of an osteoporosis detection model based on panoramic radiography.

Keywords: computer-assisted; fractals; image processing; mandible; osteoporosis; panoramic; radiography.

Figures

Figure 1

Flowchart of image processing and feature analysis. The process inside the rectangular box represents image processing using ImageJ. The black area represents the image processing procedure and line arrow represents feature analysis. Obtaining the ROIs and feature analysis were performed using MATLAB. ROIs, regions of interest.

Figure 2

A total of 4 regions of interest (ROIs) were selected in the panoramic radiography: the centre of the condylar head (ROI 1), centre of the ramus (ROI 2), and area below and between 2 molars (ROI 3). The endosteal margin area (ROI 4) was selected horizontally from the intersection point of the oblique line and ramus to the midpoint between the image center and the intersection point.

Figure 3

Regions of interest (ROIs) 4 (the endosteal margin area) was obtained by a customized program using the 5 steps below. (a) ROI containing endosteal margin area. (b) User defined points (black circles) along the endosteal margin. (c) Smooth spline curves (dotted curve) connecting the user-defined points and curved ROI 3 mm above (white curve) and below (white curve) the spline curves. (d) Stretched rectangular ROI. (e) Redefined ROI to avoid coming into contact with the inferior border; the dotted white line represents the redefined ROI. (f) Final ROI with upper and lower boundaries trimmed.

Figure 4

Image processing results according to different upsampling (enlargement with interpolation) ratio with Gaussian filter (35 sigma and 33 filter size). When resampled to 400%, the binary and skeletonized images showed optimal results. (c), (d) were resized to 400% after the image processing for comparison (a) Original image (5 × 5 mm, left) and 400% upsampled image (right); (b) Binary and skeletonized images (original image); (c) Binary and skeletonized images (400% upsampled); (d) Binary and skeletonized images (1600% upsampled).

Figure 5

Original and skeletonized images show the pattern difference between normal and osteoporotic patients. The skeletonized images of osteoporotic patients show unorganized and porous structures than found in the normal group. All images were processed after 400% upsampling. (a) Original image; (b) skeletonized image.

Figure 6

Decision tree algorithm identifying osteoporotic and normal patients. The decision tree was composed of N.Nd/N.Tm, TSL/total area and N.Nd/TSL of the endosteal margin area, and exhibited an accuracy of 96.2% for screening osteoporosis. Classification results were represented using boxes and the wrong results were coloured with gray. N, number; Tm, termini; Nd, nodes; TSL, total length of struts.