Local problem forests: Classifier training for locally limited sub-problems using spectral clustering

Voxel-wise classification for image segmentation often suffers the drawback, that the learnt global classification model only insufficiently captures sub-problems locally limited in problem space. We propose a novel method using spectral clustering to partition the global problem space into strongly connected clusters representing sub-problems. With fuzzy training set sampling, overlapping local problem classifiers are subsequently trained for each. Evaluation on a database of 37 magnetic resonance images displaying ischemic stroke lesions shows a significant improvement in segmentation accuracy compared to standard decision forest.