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.
|Title of host publication||Proceedings - International Symposium on Biomedical Imaging|
|Number of pages||4|
|Publisher||IEEE Computer Society|
|Publication status||Published - 21.07.2015|