29 March 2007 Multispectral brain tumor segmentation based on histogram model adaptation
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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.
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Jan Rexilius, Horst K. Hahn, Jan Klein, Markus G. Lentschig, Heinz-Otto Peitgen, "Multispectral brain tumor segmentation based on histogram model adaptation", Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 65140V (29 March 2007); doi: 10.1117/12.709410; https://doi.org/10.1117/12.709410
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