Spinal deformity detection employing back propagation on neural network

• Main Authors: Kim, H., Tan, J. K., Ishikawa, S., Khalid, Marzuki, Viergever, M., Otsuka, Y., Shinomiya, T.
• Other Authors: S., Singh
• Format: Book Section
• Published: Springer 2005
• Subjects: QH426 Genetics
• Online Access: http://eprints.utm.my/id/eprint/7172/; https://link.springer.com/chapter/10.1007%2F11552499_79

We propose a new technique for automatic spinal deformity detection from moire topographic images. Normally the moire stripes of a human body show a symmetric pattern. According to the progress of the deformity of a spine, asymmetry becomes larger. Numerical representation of the degree of asymmetry is therefore useful in evaluating the deformity. Displacement of local centroids and difference of gray value are calculated between the left-hand side and the right-hand side regions of the moire images with respect to the extracted middle line. Extracted 4 feature vectors (mean value and standard deviation from the each displacement) from the left-hand side and right-hand side rectangle areas apply to train a neural network. An experiment was performed employing 1,200 real moire images and 90.3% of the images were classified correctly.