Tumor segmentation in brain MRI by sparse optimization

Shandong Wu; David J. Rippe; Nicholas G. Avgeropoulos

doi:10.1117/12.2007086

13 March 2013 Tumor segmentation in brain MRI by sparse optimization

Shandong Wu, David J. Rippe, Nicholas G. Avgeropoulos

Proceedings Volume 8669, Medical Imaging 2013: Image Processing; 86691H (2013) https://doi.org/10.1117/12.2007086
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

In this work we propose a novel method for brain tumor segmentation in MRI by adapting the sparse optimization techniques. The core of the method lies in the subspace decomposition of the tissue feature space constituted by the brain MR images. The tumor-grown MRI slices can be viewed as a corrupted observation, which therefore can be decomposed into two components: the low-rank normal brain tissue structures and the sparse corruption/error that is due to the developed tumor. Through performing rank decomposition the corruption/error can be spotted out, thus giving rise to an initial segmentation of tumor. Our method requires no model learning. Experiments are performed on a data set of 12 subjects and the segmentation agreement is 0.86 in terms of the Dice’s similarity coefficient in comparison with the manual segmentation that is performed by a 15-year experienced radiologist. The proposed method represents an efficient mode for brain tumor segmentation that may be potentially incorporated in automated or semi-automatic segmentation systems in the clinical workflow.

Citation Download Citation

Shandong Wu, David J. Rippe, and Nicholas G. Avgeropoulos "Tumor segmentation in brain MRI by sparse optimization", Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86691H (13 March 2013); https://doi.org/10.1117/12.2007086

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