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Spinal deformity detection employing back propagation on neural network

Kim, H. and Tan, J. K. and Ishikawa, S. and Khalid, Marzuki and Viergever, M. and Otsuka, Y. and Shinomiya, T. (2005) Spinal deformity detection employing back propagation on neural network. In: Pattern Recognition and Image Analysis. Lecture Notes in Computer Science, 3687 . Springer , Berlin / Heidelberg, pp. 719-725. ISBN 978-3-540-28833-6

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Official URL: https://link.springer.com/chapter/10.1007%2F115524...


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.

Item Type:Book Section
Uncontrolled Keywords:neural network, spinal deformity, geometric index
Subjects:Q Science > QH Natural history > QH426 Genetics
ID Code:7172
Deposited By: Surayahani Abu Bakar
Deposited On:20 Jan 2009 16:33
Last Modified:13 Aug 2017 15:59

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