title: Manifold Learning in MR spectroscopy using nonlinear dimensionality reduction and unsupervised clustering.
abstract: OBJECTIVE
  To investigate whether nonlinear dimensionality reduction improves unsupervised classification of (1) H MRS brain tumor data compared with a linear method.
  
  
  METHODS
  In vivo single-voxel (1) H magnetic resonance spectroscopy (55 patients) and (1) H magnetic resonance spectroscopy imaging (MRSI) (29 patients) data were acquired from histopathologically diagnosed gliomas. Data reduction using Laplacian eigenmaps (LE) or independent component analysis (ICA) was followed by k-means clustering or agglomerative hierarchical clustering (AHC) for unsupervised learning to assess tumor grade and for tissue type segmentation of MRSI data.
  
  
  RESULTS
  An accuracy of 93% in classification of glioma grade II and grade IV, with 100% accuracy in distinguishing tumor and normal spectra, was obtained by LE with unsupervised clustering, but not with the combination of k-means and ICA. With (1) H MRSI data, LE provided a more linear distribution of data for cluster analysis and better cluster stability than ICA. LE combined with k-means or AHC provided 91% accuracy for classifying tumor grade and 100% accuracy for identifying normal tissue voxels. Color-coded visualization of normal brain, tumor core, and infiltration regions was achieved with LE combined with AHC.
  
  
  CONCLUSIONS
  The LE method is promising for unsupervised clustering to separate brain and tumor tissue with automated color-coding for visualization of (1) H MRSI data after cluster analysis.
authors.affiliation: Neurosciences Research Centre, Cardiovascular and Cell Sciences Institute, St. George's University of London, London, UK.
authors.forename: Guang
authors.initials: G
authors.lastname: Yang
authors.affiliation: Neurosciences Research Centre, Cardiovascular and Cell Sciences Institute, St. George's University of London, London, UK.
authors.forename: Felix
authors.initials: F
authors.lastname: Raschke
authors.affiliation: Neurosciences Research Centre, Cardiovascular and Cell Sciences Institute, St. George's University of London, London, UK.
authors.forename: Thomas R
authors.initials: TR
authors.lastname: Barrick
authors.affiliation: Neurosciences Research Centre, Cardiovascular and Cell Sciences Institute, St. George's University of London, London, UK.
authors.forename: Franklyn A
authors.initials: FA
authors.lastname: Howe
mesh_terms: D000328:Adult; D000465:Algorithms; D001932:Brain Neoplasms / Q000737:chemistry / Q000473:pathology; D016000:Cluster Analysis; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated
keywords: Laplacian eigenmaps; dimensionality reduction; magnetic resonance spectroscopic imaging; magnetic resonance spectroscopy; pattern recognition