title: Analysis of neoplastic lesions in magnetic resonance imaging using self-organizing maps.
abstract: OBJECTIVE
  To provide an improved method for the identification and analysis of brain tumors in MRI scans using a semi-automated computational approach, that has the potential to provide a more objective, precise and quantitatively rigorous analysis, compared to human visual analysis.
  
  
  BACKGROUND
  Self-Organizing Maps (SOM) is an unsupervised, exploratory data analysis tool, which can automatically domain an image into selfsimilar regions or clusters, based on measures of similarity. It can be used to perform image-domain of brain tissue on MR images, without prior knowledge.
  
  
  METHODS
  We used SOM to analyze T1, T2 and FLAIR acquisitions from two MRI machines in our service from 14 patients with brain tumors confirmed by biopsies--three lymphomas, six glioblastomas, one meningioma, one ganglioglioma, two oligoastrocytomas and one astrocytoma. The SOM software was used to analyze the data from the three image acquisitions from each patient and generated a self-organized map for each containing 25 clusters.
  
  
  RESULTS
  Damaged tissue was separated from the normal tissue using the SOM technique. Furthermore, in some cases it allowed to separate different areas from within the tumor--like edema/peritumoral infiltration and necrosis. In lesions with less precise boundaries in FLAIR, the estimated damaged tissue area in the resulting map appears bigger.
  
  
  CONCLUSIONS
  Our results showed that SOM has the potential to be a powerful MR imaging analysis technique for the assessment of brain tumors.
authors.affiliation: University of Campinas (UNICAMP), Brazil. Electronic address: drkult@gmail.com.
authors.forename: Paulo Afonso
authors.initials: PA
authors.lastname: Mei
authors.affiliation: University of Sao Paulo (USP), Brazil.
authors.forename: Cleyton
authors.initials: C
authors.lastname: de Carvalho Carneiro
authors.affiliation: Commonwealth Scientific and Industrial Research Organization (CSIRO), Australia.
authors.forename: Stephen J
authors.initials: SJ
authors.lastname: Fraser
authors.affiliation: University of Campinas (UNICAMP), Brazil.
authors.forename: Li Li
authors.initials: LL
authors.lastname: Min
authors.affiliation: University of Campinas (UNICAMP), Brazil.
authors.forename: Fabiano
authors.initials: F
authors.lastname: Reis
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001932:Brain Neoplasms / Q000145:classification / Q000473:pathology*; D005260:Female; D005909:Glioblastoma / Q000473:pathology*; D005910:Glioma / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D008577:Meningeal Neoplasms / Q000473:pathology; D008579:Meningioma / Q000473:pathology; D008875:Middle Aged; D012372:ROC Curve
keywords: Brain; MRI; Magnetic resonance imaging; Neoplastic; SOM; Self-organizing maps; Tumors