Abstract
Brain tumor segmentation and labeling is a challenging task in medical imaging. In this paper, a novel patch based dictionary learning algorithm for automatic multi-label brain tumor segmentation is proposed. Based on image reconstruction, we present coupled dictionaries, one dictionary of grayscale brain tumor image patches and one dictionary of tumor labels, which can then be used for automatic multi-label brain tumor segmentation of a test image data. The dictionaries are learned from training images of BraTS-MICCAI and the SPL/NSG brain tumor databases. The label dictionary is proposed to select foreground and background labels for automatic graph-cut segmentation. For quantitative evaluation, five different metric scores are computed using the online evaluation tool provided by the BraTS organizers. Experimental results demonstrate that the proposed approach achieves accurate results and outperforms most of the state-of-the-art methods cited in BraTS-MICCAI challenge.
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Al-Shaikhli, S.D.S., Yang, M.Y., Rosenhahn, B. (2014). Coupled Dictionary Learning for Automatic Multi-Label Brain Tumor Segmentation in Flair MRI images. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8887. Springer, Cham. https://doi.org/10.1007/978-3-319-14249-4_46
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DOI: https://doi.org/10.1007/978-3-319-14249-4_46
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