27 March 2009 Multi-channel MRI segmentation with graph cuts using spectral gradient and multidimensional Gaussian mixture model
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Proceedings Volume 7259, Medical Imaging 2009: Image Processing; 72593X (2009) https://doi.org/10.1117/12.811108
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
A new segmentation framework is presented taking advantage of multimodal image signature of the different brain tissues (healthy and/or pathological). This is achieved by merging three different modalities of gray-level MRI sequences into a single RGB-like MRI, hence creating a unique 3-dimensional signature for each tissue by utilising the complementary information of each MRI sequence. Using the scale-space spectral gradient operator, we can obtain a spatial gradient robust to intensity inhomogeneity. Even though it is based on psycho-visual color theory, it can be very efficiently applied to the RGB colored images. More over, it is not influenced by the channel assigment of each MRI. Its optimisation by the graph cuts paradigm provides a powerful and accurate tool to segment either healthy or pathological tissues in a short time (average time about ninety seconds for a brain-tissues classification). As it is a semi-automatic method, we run experiments to quantify the amount of seeds needed to perform a correct segmentation (dice similarity score above 0.85). Depending on the different sets of MRI sequences used, this amount of seeds (expressed as a relative number in pourcentage of the number of voxels of the ground truth) is between 6 to 16%. We tested this algorithm on brainweb for validation purpose (healthy tissue classification and MS lesions segmentation) and also on clinical data for tumours and MS lesions dectection and tissues classification.
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Jérémy Lecoeur, Jean-Christophe Ferré, D. Louis Collins, Sean P. Morrisey, Christian Barillot, "Multi-channel MRI segmentation with graph cuts using spectral gradient and multidimensional Gaussian mixture model", Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72593X (27 March 2009); doi: 10.1117/12.811108; https://doi.org/10.1117/12.811108
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