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Multi Channel MRI Segmentation With Graph Cuts Using Spectral Gradient And Multidimensional Gaussian Mixture Model

文献类型： 会议论文

第一作者： Jeremy Lecoeur

作者： Jeremy Lecoeur 1 ; Jean-Christophe Ferre 2 ; D. Louis Collins 3 ; Sean Morrissey 4 ; Christian Barillot 1 ;

作者机构： 1.INRIA, VisAGeS Unit/Project, IRISA, Rennes, France

2.Department of Neuroradiology, Pontchaillou University Hospital, Rennes, France

3.Montreal Neurological Institute, McGill University, Montreal, Canada

4.Departement of Neurology, University Hospital Pontchaillou, Rennes, France

关键词： MRI;RGB;tissues

会议名称： SPIE Conference on Medical Imaging 2009

主办单位：

页码： 72593:L1-72593X:11

摘要： 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 inho-mogeneity. 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.

分类号： N52

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