Osteoporosis is a common health problem that affects one-third of women over the age of 50 and it may not be detected until bone fractures occur. Osteoporosis is low bone mass and microarchitectural deterioration of bone tissue, which affects bone fragility and raises fracture risks. Early mandible bone osteoporosis detection could help reduce the risk of jaw fracture and dental implant failure. To solve this problem, a diagnostic algorithm for automatic detection of osteoporosis in Cone-Beam Computed Tomography (CBCT) images is presented and 120 mandible CBCT images of 50-85 year-old women have been utilized. These images are classified into two classes: normal and osteoporotic. Their classification is based on the T-score which derives from the Dual-Energy X-ray Absorptiometry (DEXA). The proposed algorithm consists of image processing, feature extraction, and Artificial Neural Network (ANN) classification. Images are segmented and edges are detected. Then, texture features are extracted from the segmented regions. Finally, a feed-forward back-propagation ANN classifier is employed. Seven parameters were involved in the experiment data preparation as input: coarseness, contrast, direction, number of edges, length of edges, mean length of edges, and the number of edge pixels. The results demonstrate the effectiveness of the proposed method. With the help of the proposed method, dentists will be able to predict osteoporosis accurately and efficiently without the need for further examination since CBCT has been widely accepted in dentistry and the dentist is the most common health care professional that elderly visit regularly.