TY - GEN
T1 - Semi-automated estimation of spinal curvature from scoliosis radiographs using difference matrix
AU - Kamath, Rakshith
AU - Areeckal, Anu Shaju
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/30
Y1 - 2020/10/30
N2 - Scoliosis is a medical condition in which there is a deformity and bending of the spine. Early detection and corrective measures can prevent severe cases, mainly in early childhood. The gold standard method for the diagnosis of scoliosis is the measurement of spine curvature using Cobb angle. In this paper, a semi-automated approach for accurate estimation of Cobb angles from scoliosis radiographs using a difference matrix is proposed. Given the landmark points of the vertebral columns in the spine, the spinal mid-line is determined by curve fitting of the 4th degree polynomial. Using a difference matrix created by calculating slopes in key points along the curve, Cobb angles are calculated. Data used in this work is obtained from the Accurate Automated Spinal Curvature Estimation (AASCE) Challenge 2019. The method is evaluated using mean absolute error and symmetric mean absolute percentage error. The proposed method gives promising results and could be used in accurate estimation of Cobb angle for the diagnosis of scoliosis.
AB - Scoliosis is a medical condition in which there is a deformity and bending of the spine. Early detection and corrective measures can prevent severe cases, mainly in early childhood. The gold standard method for the diagnosis of scoliosis is the measurement of spine curvature using Cobb angle. In this paper, a semi-automated approach for accurate estimation of Cobb angles from scoliosis radiographs using a difference matrix is proposed. Given the landmark points of the vertebral columns in the spine, the spinal mid-line is determined by curve fitting of the 4th degree polynomial. Using a difference matrix created by calculating slopes in key points along the curve, Cobb angles are calculated. Data used in this work is obtained from the Accurate Automated Spinal Curvature Estimation (AASCE) Challenge 2019. The method is evaluated using mean absolute error and symmetric mean absolute percentage error. The proposed method gives promising results and could be used in accurate estimation of Cobb angle for the diagnosis of scoliosis.
UR - https://www.scopus.com/pages/publications/85099690108
UR - https://www.scopus.com/pages/publications/85099690108#tab=citedBy
U2 - 10.1109/DISCOVER50404.2020.9278054
DO - 10.1109/DISCOVER50404.2020.9278054
M3 - Conference contribution
AN - SCOPUS:85099690108
T3 - 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020 - Proceedings
SP - 147
EP - 152
BT - 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020
Y2 - 30 October 2020 through 31 October 2020
ER -