Share Email Print

Proceedings Paper

Multispectral brain tumor segmentation based on histogram model adaptation
Author(s): Jan Rexilius; Horst K. Hahn; Jan Klein; Markus G. Lentschig; Heinz-Otto Peitgen
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper 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.

Paper Details

Date Published: 29 March 2007
PDF: 10 pages
Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 65140V (29 March 2007); doi: 10.1117/12.709410
Show Author Affiliations
Jan Rexilius, MeVis Research (Germany)
Horst K. Hahn, MeVis Research (Germany)
Jan Klein, MeVis Research (Germany)
Markus G. Lentschig, Ctr. for Magnetic Resonance Imaging (Germany)
Heinz-Otto Peitgen, MeVis Research (Germany)

Published in SPIE Proceedings Vol. 6514:
Medical Imaging 2007: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?