TY - GEN
T1 - FingerNet
T2 - 11th IEEE Biomedical Circuits and Systems Conference, BioCAS 2015
AU - Lee, Sungmin
AU - Choi, Minsuk
AU - Choi, Hyun Soo
AU - Park, Moon Seok
AU - Yoon, Sungroh
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/4
Y1 - 2015/12/4
N2 - Radiographic image assessment is the most common method used to measure physical maturity and diagnose growth disorders, hereditary diseases and rheumatoid arthritis, with hand radiography being one of the most frequently used techniques due to its simplicity and minimal exposure to radiation. Finger joints are considered as especially important factors in hand skeleton examination. Although several automation methods for finger joint detection have been proposed, low accuracy and reliability are hindering full-scale adoption into clinical fields. In this paper, we propose FingerNet, a novel approach for the detection of all finger joints from hand radiograph images based on convolutional neural networks, which requires little user intervention. The system achieved 98.02% average detection accuracy for 130 test data sets containing over 1,950 joints. Further analysis was performed to verify the system robustness against factors such as epiphysis and metaphysis in different age groups.
AB - Radiographic image assessment is the most common method used to measure physical maturity and diagnose growth disorders, hereditary diseases and rheumatoid arthritis, with hand radiography being one of the most frequently used techniques due to its simplicity and minimal exposure to radiation. Finger joints are considered as especially important factors in hand skeleton examination. Although several automation methods for finger joint detection have been proposed, low accuracy and reliability are hindering full-scale adoption into clinical fields. In this paper, we propose FingerNet, a novel approach for the detection of all finger joints from hand radiograph images based on convolutional neural networks, which requires little user intervention. The system achieved 98.02% average detection accuracy for 130 test data sets containing over 1,950 joints. Further analysis was performed to verify the system robustness against factors such as epiphysis and metaphysis in different age groups.
UR - http://www.scopus.com/inward/record.url?scp=84962756303&partnerID=8YFLogxK
U2 - 10.1109/BioCAS.2015.7348440
DO - 10.1109/BioCAS.2015.7348440
M3 - Conference contribution
AN - SCOPUS:84962756303
T3 - IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings
BT - IEEE Biomedical Circuits and Systems Conference
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 22 October 2015 through 24 October 2015
ER -