• Journal of Korea Multimedia Society
• /
• v.21 no.12
• /
• pp.1407-1416
• /
• 2018

The purpose of this study was to train a model for the ulna and radius bone segmentation based on Convolutional Neural Networks and to verify the segmentation model. The data consisted of 840 training data, 210 tuning data, and 200 verification data. The learning model for the ulna and radius bone bwas based on U-Net (19 convolutional and 8 maximum pooling) and trained with 8 batch sizes, 0.0001 learning rate, and 200 epochs. As a result, the average sensitivity of the training data was 0.998, the specificity was 0.972, the accuracy was 0.979, and the Dice's similarity coefficient was 0.968. In the validation data, the average sensitivity was 0.961, specificity was 0.978, accuracy was 0.972, and Dice's similarity coefficient was 0.961. The performance of deep convolutional neural network based models for the segmentation was good for ulna and radius bone.

• Journal of Korea Multimedia Society
• /
• v.12 no.9
• /
• pp.1233-1241
• /
• 2009

Over the 50 percents of women who are older than 45 years have osteoporosis. Because people hardly recognize this disease by themselves, the researches that measure bone mineral density have been doing widely to detect osteoporosis in the early stage. The most widely used methods for bone mineral density measurement are based on the X-ray imaging. Among them, DEXA(Dual-energy X-ray Absorptiometry) imaging is one of the important methods in bone mineral density measurement. DEXA images are useful methods to increase diagnosis efficiency by reducing anatomic noise as two images obtained from two different energy levels. However, it has some problems to a calibration parameter determined by the heuristic method for bone extraction. In this paper, we propose the method to extract bone in DEXA image using calibration parameter based on anatomic attenuation coefficient. The experimental results reveal that the proposed method is effective.