Automated classification of mandibular cortical bone on dental panoramic radiographs for early detection of osteoporosis

Kazuki Horiba; Chisako Muramatsu; Tatsuro Hayashi; Tatsumasa Fukui; Takeshi Hara; Akitoshi Katsumata D.D.S.; Hiroshi Fujita

doi:10.1117/12.2081512

20 March 2015 Automated classification of mandibular cortical bone on dental panoramic radiographs for early detection of osteoporosis

Kazuki Horiba, Chisako Muramatsu, Tatsuro Hayashi, Tatsumasa Fukui, Takeshi Hara, Akitoshi Katsumata D.D.S., Hiroshi Fujita

Author Affiliations +

Proceedings Volume 9414, Medical Imaging 2015: Computer-Aided Diagnosis; 94142J (2015) https://doi.org/10.1117/12.2081512
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

Findings on dental panoramic radiographs (DPRs) have shown that mandibular cortical index (MCI) based on the morphology of mandibular inferior cortex was significantly correlated with osteoporosis. MCI on DPRs can be categorized into one of three groups and has the high potential for identifying patients with osteoporosis. However, most DPRs are used only for diagnosing dental conditions by dentists in their routine clinical work. Moreover, MCI is not generally quantified but assessed subjectively. In this study, we investigated a computer-aided diagnosis (CAD) system that automatically classifies mandibular cortical bone for detection of osteoporotic patients at early stage. First, an inferior border of mandibular bone was detected by use of an active contour method. Second, regions of interest including the cortical bone are extracted and analyzed for its thickness and roughness. Finally, support vector machine (SVM) differentiate cases into three MCI categories by features including the thickness and roughness. Ninety eight DPRs were used to evaluate our proposed scheme. The number of cases classified to Class I, II, and III by a dental radiologist are 56, 25 and 17 cases, respectively. Experimental result based on the leave-one-out cross-validation evaluation showed that the sensitivities for the classes I, II, and III were 94.6%, 57.7% and 94.1%, respectively. Distribution of the groups in the feature space indicates a possibility of MCI quantification by the proposed method. Therefore, our scheme has a potential in identifying osteoporotic patients at an early stage.

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Kazuki Horiba, Chisako Muramatsu, Tatsuro Hayashi, Tatsumasa Fukui, Takeshi Hara, Akitoshi Katsumata D.D.S., and Hiroshi Fujita "Automated classification of mandibular cortical bone on dental panoramic radiographs for early detection of osteoporosis", Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94142J (20 March 2015); https://doi.org/10.1117/12.2081512

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