Multi Channel MRI Segmentation With Graph Cuts Using Spectral Gradient And Multidimensional Gaussian Mixture ModelReport as inadecuate

Multi Channel MRI Segmentation With Graph Cuts Using Spectral Gradient And Multidimensional Gaussian Mixture Model - Download this document for free, or read online. Document in PDF available to download.

* Corresponding author 1 VisAGeS - Vision, Action et Gestion d-informations en Santé IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE, INSERM - Institut National de la Santé et de la Recherche Médicale : U746, Inria Rennes – Bretagne Atlantique 2 Service de neurochirurgie Rennes 3 Service de radiologie et imagerie médicale Rennes 4 Montreal Neurological Institute Montréal 5 BIC - McConnell Brain Imaging Center 6 Service de neurologie Rennes

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 assignment of each MRI. Its optimisation by the graph cut 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 voxelsof 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 detection and tissue classification.

Author: Jérémy Lecoeur - Jean-Christophe Ferré - D. Louis Collins - Sean Patrick Morrissey - Christian Barillot -



Related documents