Oral lesions are important findings on computed tomography (CT) images. In this study, a fully automatic method to detect oral lesions in mandibular region from dental CT images is proposed. Two methods were developed to recognize two types of lesions namely (1) Close border (CB) lesions and (2) Open border (OB) lesions, which cover most of the lesion types that can be found on CT images. For the detection of CB lesions, fifteen features were extracted from each initial lesion candidates and multi layer perceptron (MLP) neural network was used to classify suspicious regions. Moreover, OB lesions were detected using a rule based image processing method, where no feature extraction or classification algorithm were used. The results were validated using a CT dataset of 52 patients, where 22 patients had abnormalities and 30 patients were normal. Using non-training dataset, CB detection algorithm yielded 71 sensitivity with 0.31 false positives per patient. Furthermore, OB detection algorithm achieved 100 sensitivity with 0.13 false positives per patient. Results suggest that, the proposed framework, which consists of two methods, has the potential to be used in clinical context, and assist radiologists for better diagnosis.
Purpose Mandibular cortical width (MCW) measured on dental panoramic radiographs (DPRs) was significantly correlated with bone mineral density. We developed a computer-aided diagnosis scheme that automatically measures MCW to assist dentists in describing a possible osteoporotic risk and suggesting further examinations. Background Early diagnosis of osteoporosis can potentially decrease the risk of fractures and improve the quality of life. Detection of thin inferior cortices of the mandible on dental panoramic radiographs could be useful for identifying postmenopausal women with low bone mineral density (BMD) or osteoporosis. The aim of our study was to assess the diagnostic efficacy of using kernel-based support vector machine (SVM) learning regarding the cortical width of the mandible on dental panoramic radiographs to identify postmenopausal women with low BMD. 