DEeP Random Walks

In this paper, we proposed distance enforced penalized (DEeP) random walks segmentation framework to delineate coupled boundaries by modifying classical random walks formulations. We take into account curves inter-dependencies and incorporate associated distances into weight function of conventional random walker. This effectively leverages segmentation of weaker boundaries guided by stronger counterparts, which is the main advantage over classical random walks techniques where the weight function is only dependent on intensity differences between connected pixels, resulting in unfavorable outcomes in the context of poor contrasted images. First, we applied our developed algorithm on synthetic data and then on cardiac magnetic resonance (MR) images for detection of myocardium borders. We obtained encouraging results and observed that proposed algorithm prevents epicardial border to leak into right ventricle or cross back into endocardial border that often observe when conventional random walker is used. We applied our method on forty cardiac MR images and quantified the results with corresponding manual traced borders as ground truths. We found the Dice coefficients 70% +/- 14% and 43% +/- 14% respectively for DEeP random walks and conventional one.