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Analyse de données d'IRM fonctionnelle rénale par quantification vectorielle

Abstract : Dynamic-Contrast-Enhanced Magnetic Resonance Imaging has a great potential for renal function assessment but has to be evaluated on a large scale before its clinical application. Registration of image sequences and segmentation of internal renal structures is mandatory in order to exploit acquisitions. We propose a reliable and user-friendly tool to partially automate these two operations. Statistical registration methods based on mutual information are tested on real data. Segmentation of cortex, medulla and cavities is performed using time-intensity curves of renal voxels in a two step process. Classifiers are first built with pixels of the slice that contains the largest proportion of renal tissue : two vector quantization algorithms, namely the K-means and the Growing Neural Gas with targeting, are used here. These classifiers are first tested on synthetic data. For real data, as no ground truth is available for result evaluation, a manual anatomical segmentation is considered as a reference. Some discrepancy criteria like overlap, extra pixels and similarity index are computed between this segmentation and functional one. The same criteria are also evaluated between the referencee and another manual segmentation. Results are comparable for the two types of comparisons. Voxels of other slices are then sorted with the optimal classifier. Generalization theory allows to bound classification error for this extension. The main advantages of functional methods are the following : considerable time-saving, easy manual intervention, good robustness and reproductibility
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Submitted on : Thursday, March 29, 2018 - 1:54:13 PM
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Béatrice Chevaillier. Analyse de données d'IRM fonctionnelle rénale par quantification vectorielle. Mathématiques générales [math.GM]. Université Paul Verlaine - Metz, 2010. Français. ⟨NNT : 2010METZ005S⟩. ⟨tel-01752677⟩



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