Multispectral analysis of the brain using magnetic resonance imaging

By T. Taxt, Arvid Lundervold. 1994

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An overview of deep learning in medical imaging focusing on MRI (2018, background): “ical imaging segmentation. In this early era, we also had the opportunity to contribute with supervised and unsupervised machine learning approaches for MR image segmentation and tissue classication [250, 251, 252, 253]. An impressive range of segmentation methods and approaches have been reported (”
Data Clustering (2008, unknown): “References [182; 124] describe a partitional clustering algorithm and a manual labeling technique to identify material classes (e.”
Automatic segmentation of non-enhancing brain tumors in magnetic resonance images (2001, methodology): “Although MR segmentation methods have been quite successful on normal tissues [23], [31], [24], [33], the actual methods of MR segmentation are still very much in the development stages for pathological tissues with some success recorded for speci c disease processes [3], [6], [18].”
Segmentation of brain parenchyma and cerebrospinal fluid in multispectral magnetic resonance images (1995, methodology): “Initial training To obtain the initial parameter estimates f (0) c ; (0) c gc=1;:::;7 we used k-means clustering [3] (k=7) of a single multispectral slice image from a single patient with normal MRI ndings, following the approach described in [53].”