Abstract
Brain tumor segmentation and quantification from MR images is a challenging task. The boundary of a tumor
and its volume are important parameters that can have direct impact on surgical treatment, radiation therapy,
or on quantitative measurements of tumor regression rates. Although a wide range of different methods has
already been proposed, a commonly accepted approach is not yet established. Today, the gold standard at many
institutions still consists of a manual tumor outlining, which is potentially subjective, and a time consuming and
tedious process.
We propose a new method that allows for fast multispectral segmentation of brain tumors. An efficient initialization
of the segmentation is obtained using a novel probabilistic intensity model, followed by an iterative
refinement of the initial segmentation. A progressive region growing that combines probability and distance
information provides a new, flexible tumor segmentation. In order to derive a robust model for brain tumors
that can be easily applied to a new dataset, we retain information not on the anatomical, but on the global
cross-subject intensity variability. Therefore, a set of multispectral histograms from different patient datasets
is registered onto a reference histogram using global affine and non-rigid registration methods. The probability
model is then generated from manual expert segmentations that are transferred to the histogram feature domain.
A forward and backward transformation of a manual segmentation between histogram and image domain allows
for a statistical analysis of the accuracy and robustness of the selected features. Experiments are carried out on
patient datasets with different tumor shapes, sizes, locations, and internal texture.
