  Cobb angle, which is a measure of spinal curvature is the standard method for
quantifying the magnitude of Scoliosis related to spinal deformity in
orthopedics. Determining the Cobb angle through manual process is subject to
human errors. In this work, we propose a methodology to measure the magnitude
of Cobb angle, which appreciably reduces the variability related to its
measurement compared to the related works. The proposed methodology is
facilitated by using a suitable new improved version of Non-Local Means for
image denoisation and Otsus automatic threshold selection for Canny edge
detection. We have selected NLM for preprocessing of the image as it is one of
the fine states of art for image denoisation and helps in retaining the image
quality. Trimmedmean, median are more robust to outliners than mean and
following this concept we observed that NLM denoising quality performance can
be enhanced by using Euclidean trimmed-mean replacing the mean. To prove the
better performance of the Non-Local Euclidean Trimmed-mean denoising filter, we
have provided some comparative study results of the proposed denoising
technique with traditional NLM and NonLocal Euclidean Medians. The experimental
results for Cobb angle measurement over intra observer and inter observer
experimental data reveals the better performance and superiority of the
proposed approach compared to the related works. MATLAB2009b image processing
toolbox was used for the purpose of simulation and verification of the proposed
methodology.
