@ARTICLE{6939729, 
author={Z. Wang and X. Zhen and K. Tay and S. Osman and W. Romano and S. Li}, 
journal={IEEE Transactions on Medical Imaging}, 
title={Regression Segmentation for  M^{3} Spinal Images}, 
year={2015}, 
volume={34}, 
number={8}, 
pages={1640-1648}, 
keywords={biomedical MRI;bone;computerised tomography;diseases;feature extraction;image segmentation;medical image processing;regression analysis;support vector machines;CT modalities;M3 spinal images;MRI;MSVR;boundary regression problem;clinical routine;clinical subjects;disc structures;high dice similarity index;high dimensional feature space;highly nonlinear mapping function;multidimensional support vector regressor;multiple anatomic planes;multiple anatomic structures;multiple imaging modalities;object boundaries;regression segmentation;segmenting spinal images;sparse kernel machines;specific modality;spinal disease diagnosis;spinal disease treatment;spinal images;substantially diverse M3 images;vertebral structures;Computed tomography;Image segmentation;Kernel;Magnetic resonance imaging;Shape;Solid modeling;Three-dimensional displays;Computed tomography (CT);disc;magnetic resonance imaging (MRI);multi-kernel;segmentation;spine;support vector regression;vertebra;0}, 
doi={10.1109/TMI.2014.2365746}, 
ISSN={0278-0062}, 
month={Aug},}