Learning-based landmarks detection for osteoporosis analysis

Erkang Cheng; Ling Zhu; Jie Yang; Azhari Azhari; Suhardjo Sitam; Xin Liang; Vasileios Megalooikonomou; Haibin Ling

doi:10.1117/12.2216455

21 March 2016 Learning-based landmarks detection for osteoporosis analysis

Erkang Cheng, Ling Zhu, Jie Yang, Azhari Azhari, Suhardjo Sitam, Xin Liang, Vasileios Megalooikonomou, Haibin Ling

Proceedings Volume 9784, Medical Imaging 2016: Image Processing; 97841X (2016) https://doi.org/10.1117/12.2216455
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

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.

Citation Download Citation

Erkang Cheng, Ling Zhu, Jie Yang, Azhari Azhari, Suhardjo Sitam, Xin Liang, Vasileios Megalooikonomou, and Haibin Ling "Learning-based landmarks detection for osteoporosis analysis", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97841X (21 March 2016); https://doi.org/10.1117/12.2216455

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