A panoramic radiography image provides not only details of teeth but also rich information about trabecular bone. Recent studies have addressed the correlation between trabecular bone structure and osteoporosis. In this paper, we collect a dataset containing 40 images from 40 different subjects, and construct a new methodology based on a two-stage classification framework that combines multiple trabecular bone regions of interest (ROIs) for osteoporosis prescreening. In the first stage, different support vector machines (SVMs) are adopted to describe different information of different ROIs. In the second stage, the output probabilities of the first stage are effectively combined by using an additional linear SVM model to make a final prediction. Based on our two stage model, we test the performance of different image features by using leave-one-out cross-valuation and analysis of variance rules. The results suggest that the proposed method with the HOG (histogram of oriented gradients) feature achieves the best overall accuracy.