|M. Pawan Kumar|
DISCRIMINATIVE PARAMETER ESTIMATION FOR RANDOM WALKS SEGMENTATION
P.-Y. Baudin, D. Goodman, P. Kumar, N. Azzabou, P.G. Carlier, N. Paragios and M. Pawan Kumar
In Proceedings of International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2013
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.