title: Influence of gray level and space discretization on brain tumor heterogeneity measures obtained from magnetic resonance images.
abstract: Tumor heterogeneity in medical imaging is a current research trend due to its potential relationship with tumor malignancy. The aim of this study is to analyze the effect of dynamic range and matrix size changes on the results of different heterogeneity measures.
  
  
  
  Four patients harboring three glioblastomas and one metastasis were considered. Sixteen textural heterogeneity measures were computed for each patient, with a configuration including co-occurrence matrices (CM) features (local heterogeneity) and run-length matrices (RLM) features (regional heterogeneity). The coefficient of variation measured agreement between the textural measures in two types of experiments: (i) fixing the matrix size and changing the dynamic range and (ii) fixing the dynamic range and changing the matrix size.
  
  
  
  None of the measures considered were robust under dynamic range changes. The CM Entropy and the RLM high gray-level run emphasis (HGRE) were the outstanding textural features due to their robustness under matrix size changes. Also, the RLM low gray-level run emphasis (LGRE) provided robust results when the dynamic range considered was sufficiently high (more than 8 levels). All of the remaining textural features were not robust.
  
  
  
  Tumor texture studies based on images with different characteristics (e.g. multi-center studies) should first fix the dynamic range to be considered. For studies involving images of different resolutions either (i) only robust measures should be used (in our study CM entropy, RLM HGRE and/or RLM LGRE) or (ii) images should be resampled to match those of the lowest resolution before computing the textural features.
authors.affiliation: Instituto de Matemática Aplicada a la Ciencia y la Ingeniería, Universidad de Castilla-La Mancha, Spain. Electronic address: david.molina@uclm.es.
authors.forename: David
authors.initials: D
authors.lastname: Molina
authors.affiliation: Instituto de Matemática Aplicada a la Ciencia y la Ingeniería, Universidad de Castilla-La Mancha, Spain.
authors.forename: Julián
authors.initials: J
authors.lastname: Pérez-Beteta
authors.affiliation: Instituto de Matemática Aplicada a la Ciencia y la Ingeniería, Universidad de Castilla-La Mancha, Spain.
authors.forename: Alicia
authors.initials: A
authors.lastname: Martínez-González
authors.affiliation: Hospital Universitario Marqués de Valdecilla and Fundación Instituto de Investigación Marqués de Valdecilla, Spain.
authors.forename: Juan
authors.initials: J
authors.lastname: Martino
authors.affiliation: Hospital Universitario Marqués de Valdecilla and Fundación Instituto de Investigación Marqués de Valdecilla, Spain.
authors.forename: Carlos
authors.initials: C
authors.lastname: Velásquez
authors.affiliation: Instituto Valenciano de Oncología, Spain.
authors.forename: Estanislao
authors.initials: E
authors.lastname: Arana
authors.affiliation: Instituto de Matemática Aplicada a la Ciencia y la Ingeniería, Universidad de Castilla-La Mancha, Spain.
authors.forename: Víctor M
authors.initials: VM
authors.lastname: Pérez-García
mesh_terms: D001921:Brain / Q000000981:diagnostic imaging; D001932:Brain Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged
keywords: Brain tumors; Coefficient of variation; Feature robustness; Textural features; Tumor heterogeneity measures