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Med Image Comput Comput Assist Interv ; 16(Pt 3): 219-26, 2013.
Article in English | MEDLINE | ID: mdl-24505764

ABSTRACT

The Random Walks (RW) algorithm is one of the most efficient and easy-to-use probabilistic segmentation methods. By combining contrast terms with prior terms, it provides accurate segmentations of medical images in a fully automated manner. However, one of the main drawbacks of using the RW algorithm is that its parameters have to be hand-tuned. we propose a novel discriminative learning framework that estimates the parameters using a training dataset. The main challenge we face is that the training samples are not fully supervised. Specifically, they provide a hard segmentation of the images, instead of a probabilistic segmentation. We overcome this challenge by treating the optimal probabilistic segmentation that is compatible with the given hard segmentation as a latent variable. This allows us to employ the latent support vector machine formulation for parameter estimation. We show that our approach significantly outperforms the baseline methods on a challenging dataset consisting of real clinical 3D MRI volumes of skeletal muscles.


Subject(s)
Algorithms , Data Interpretation, Statistical , Image Interpretation, Computer-Assisted/methods , Imaging, Three-Dimensional/methods , Magnetic Resonance Imaging/methods , Muscle, Skeletal/anatomy & histology , Pattern Recognition, Automated/methods , Artificial Intelligence , Discriminant Analysis , Humans , Image Enhancement/methods , Reproducibility of Results , Sensitivity and Specificity
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