21 March 2016 Learning-based landmarks detection for osteoporosis analysis
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Abstract
Osteoporosis is the common cause for a broken bone among senior citizens. Early diagnosis of osteoporosis requires routine examination which may be costly for patients. A potential low cost diagnosis is to identify a senior citizen at high risk of osteoporosis by pre-screening during routine dental examination. Therefore, osteoporosis analysis using dental radiographs severs as a key step in routine dental examination. The aim of this study is to localize landmarks in dental radiographs which are helpful to assess the evidence of osteoporosis. We collect eight landmarks which are critical in osteoporosis analysis. Our goal is to localize these landmarks automatically for a given dental radiographic image. To address the challenges such as large variations of appearances in subjects, in this paper, we formulate the task into a multi-class classification problem. A hybrid feature pool is used to represent these landmarks. For the discriminative classification problem, we use a random forest to fuse the hybrid feature representation. In the experiments, we also evaluate the performances of individual feature component and the hybrid fused feature. Our proposed method achieves average detection error of 2:9mm.
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Erkang Cheng, Erkang Cheng, Ling Zhu, Ling Zhu, Jie Yang, Jie Yang, Azhari Azhari, Azhari Azhari, Suhardjo Sitam, Suhardjo Sitam, Xin Liang, Xin Liang, Vasileios Megalooikonomou, Vasileios Megalooikonomou, Haibin Ling, Haibin Ling, "Learning-based landmarks detection for osteoporosis analysis", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97841X (21 March 2016); doi: 10.1117/12.2216455; https://doi.org/10.1117/12.2216455
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