A brain tumor segmentation framework based on outlier detection

Marcel Prastawa, Elizabeth Bullitt, Sean Ho, Guido Gerig

    Research output: Contribution to journalArticlepeer-review

    Abstract

    This paper describes a framework for automatic brain tumor segmentation from MR images. The detection of edema is done simultaneously with tumor segmentation, as the knowledge of the extent of edema is important for diagnosis, planning, and treatment. Whereas many other tumor segmentation methods rely on the intensity enhancement produced by the gadolinium contrast agent in the T1-weighted image, the method proposed here does not require contrast enhanced image channels. The only required input for the segmentation procedure is the T2 MR image channel, but it can make use of any additional non-enhanced image channels for improved tissue segmentation. The segmentation framework is composed of three stages. First, we detect abnormal regions using a registered brain atlas as a model for healthy brains. We then make use of the robust estimates of the location and dispersion of the normal brain tissue intensity clusters to determine the intensity properties of the different tissue types. In the second stage, we determine from the T2 image intensities whether edema appears together with tumor in the abnormal regions. Finally, we apply geometric and spatial constraints to the detected tumor and edema regions. The segmentation procedure has been applied to three real datasets, representing different tumor shapes, locations, sizes, image intensities, and enhancement.

    Original languageEnglish (US)
    Pages (from-to)275-283
    Number of pages9
    JournalMedical Image Analysis
    Volume8
    Issue number3
    DOIs
    StatePublished - Sep 2004

    Keywords

    • Automatic brain segmentation
    • Brain tumor segmentation
    • Level-set evolution
    • Outlier detection
    • Robust estimation

    ASJC Scopus subject areas

    • Radiological and Ultrasound Technology
    • Radiology Nuclear Medicine and imaging
    • Computer Vision and Pattern Recognition
    • Health Informatics
    • Computer Graphics and Computer-Aided Design

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