TY - GEN
T1 - Computerized radiogrammetry of third metacarpal using watershed and active appearance model
AU - Areeckal, Anu Shaju
AU - Sam, Mathew
AU - David, S. Sumam
N1 - Funding Information:
the healthy and low bone mass groups, respectively. Whereas AAM showed a mean absolute error of 0.47 mm and 0.54 mm in the healthy and low bone mass groups, respectively. The proposed segmentation technique was found to outperform AAM and hence could be used efficiently for diagnosis of osteoporosis using third metacarpal radiogrammetry. ACKNOWLEDGMENT We would like to thank TEQIP-II Office, National Institute of Technology Karnataka, Surathkal, India, for supporting data collection, and the Institutional Ethics Committee, Kasturba Medical College Hospital, Mangalore, Manipal Academy of Higher Education, India, for approving the study protocol. We would like to thank the Department of Orthopedics, District Wenlock Hospital, Mangalore, India and Tejaswini Hospital, Mangalore, India, for their help in this study.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/4/27
Y1 - 2018/4/27
N2 - Osteoporosis is a bone disorder, causing loss of bone mass and increased risk of fragility fracture. Osteoporosis can be diagnosed at a low cost using computerized metacarpal radiogrammetry of the third metacarpal bone of hand X-ray images. The most widely used methods for segmentation of hand bones are deformable models such as Active Shape Model (ASM), Active Appearance Model (AAM), etc. that make use of prior information of the shape and appearance of the object. However, due to the presence of other metacarpal bones having similar shape and size in its proximity, segmentation of third metacarpal bone in isolation becomes challenging and the deformable methods could fail. In this paper, we propose a method to automatically locate and segment the third metacarpal bone using marker-controlled watershed segmentation. Radio-grammetric measurements are determined automatically from the shaft of the third metacarpal bone and can be used to derive bone indices for the detection of bone loss due to osteoporosis. The measurements obtained from the proposed method and AAM are compared with ground truth. The results obtained show that the proposed approach is an efficient method for automated radiogrammetry of third metacarpal bone that can be used as a low cost tool for the early diagnosis of osteoporosis.
AB - Osteoporosis is a bone disorder, causing loss of bone mass and increased risk of fragility fracture. Osteoporosis can be diagnosed at a low cost using computerized metacarpal radiogrammetry of the third metacarpal bone of hand X-ray images. The most widely used methods for segmentation of hand bones are deformable models such as Active Shape Model (ASM), Active Appearance Model (AAM), etc. that make use of prior information of the shape and appearance of the object. However, due to the presence of other metacarpal bones having similar shape and size in its proximity, segmentation of third metacarpal bone in isolation becomes challenging and the deformable methods could fail. In this paper, we propose a method to automatically locate and segment the third metacarpal bone using marker-controlled watershed segmentation. Radio-grammetric measurements are determined automatically from the shaft of the third metacarpal bone and can be used to derive bone indices for the detection of bone loss due to osteoporosis. The measurements obtained from the proposed method and AAM are compared with ground truth. The results obtained show that the proposed approach is an efficient method for automated radiogrammetry of third metacarpal bone that can be used as a low cost tool for the early diagnosis of osteoporosis.
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U2 - 10.1109/ICIT.2018.8352401
DO - 10.1109/ICIT.2018.8352401
M3 - Conference contribution
AN - SCOPUS:85046976322
T3 - Proceedings of the IEEE International Conference on Industrial Technology
SP - 1490
EP - 1495
BT - Proceedings - 2018 IEEE International Conference on Industrial Technology, ICIT 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Conference on Industrial Technology, ICIT 2018
Y2 - 19 February 2018 through 22 February 2018
ER -