RESUMEN
BACKGROUND: Osteoporosis is a systemic skeletal disease characterized by low bone mineral density (BMD) and micro-architectural deterioration of bone tissue, leading to bone fragility and increased fracture risk. Since Panoramic image is a feasible and relatively routine imaging technique in dentistry; it could provide an opportunistic chance for screening osteoporosis. In this regard, numerous panoramic derived indices have been developed and suggested for osteoporosis screening. Jaw trabecular pattern is one of the main bone strength factors and trabecular bone pattern assessment is important factor in bone quality analysis. Texture analysis applied to trabecular bone images offers an ability to exploit the information present on conventional radiographs. OBJECTIVE: The purpose of this study was to evaluate the relationship between Jaw trabecular pattern in panoramic image and osteoporosis based on image texture analyzing using local binary pattern. MATERIAL AND METHODS: An experiment is evaluated in this paper based on a real hand-captured database of panoramic radiograph images from osteoporosis and non-osteoporosis person in Namazi Hospital, Shiraz, Iran .An approach is proposed for osteoporosis diagnosis consisting of two steps. First, modified version of local binary patterns is used to extract discriminative features from jaw panoramic radiograph images. Then, classification is done using different classifiers. RESULTS: Comparative results show that the proposed approach provides classification accuracy about 99.6%, which is higher than many state-of-the-art methods. CONCLUSION: High classification accuracy, low computational complexity, multi-resolution and rotation invariant are among advantages of our proposed approach.
RESUMEN
BACKGROUND: Temporomandibular joint disorder (TMD) might be manifested as structural changes in bone through modification, adaptation or direct destruction. We propose to use Local Binary Pattern (LBP) characteristics and histogram-oriented gradients on the recorded images as a diagnostic tool in TMD assessment. MATERIAL AND METHODS: CBCT images of 66 patients (132 joints) with TMD and 66 normal cases (132 joints) were collected and 2 coronal cut prepared from each condyle, although images were limited to head of mandibular condyle. In order to extract features of images, first we use LBP and then histogram of oriented gradients. To reduce dimensionality, the linear algebra Singular Value Decomposition (SVD) is applied to the feature vectors matrix of all images. For evaluation, we used K nearest neighbor (K-NN), Support Vector Machine, Naïve Bayesian and Random Forest classifiers. We used Receiver Operating Characteristic (ROC) to evaluate the hypothesis. RESULTS: K nearest neighbor classifier achieves a very good accuracy (0.9242), moreover, it has desirable sensitivity (0.9470) and specificity (0.9015) results, when other classifiers have lower accuracy, sensitivity and specificity. CONCLUSION: We proposed a fully automatic approach to detect TMD using image processing techniques based on local binary patterns and feature extraction. K-NN has been the best classifier for our experiments in detecting patients from healthy individuals, by 92.42% accuracy, 94.70% sensitivity and 90.15% specificity. The proposed method can help automatically diagnose TMD at its initial stages.