Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classiffication in Combination with Hierarchical Conditional Random Field Regularization

Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.

Landscapes of Poverty and Environment in Lao PDR

A key challenge for land change science is linking land cover information to human-environment interactions over larger spatial areas. Crucial information on land use types and people involved is still lacking. In Lao PDR, a country facing rapid and multilevel land change processes, this lack of information hinders evidence-based policy- and decision-making. We present a new approach for the description of landscape mosaics on national level and relate it to village level Population Census information. Results showed that swidden agricultural landscapes, involving 17% of the population, dominate 28% of the country, while permanent agricultural landscapes involve 74% of the population in 29% of the country.