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Histological Fractal-Based Classification of Brain Tumors

  • Omar S. Al-KadiEmail author
  • Antonio Di Ieva
Chapter
Part of the Springer Series in Computational Neuroscience book series (NEUROSCI)

Abstract

The structural complexity of brain tumor tissue represents a major challenge for effective histopathological diagnosis. Tumor vasculature is known to be heterogeneous and mixtures of patterns are usually present. Therefore, extracting key descriptive features for accurate quantification is not a straightforward task. Several steps are involved in the texture analysis process where tissue heterogeneity contributes to the variability of the results. One of the interesting aspects of the brain lies in its fractal nature. Many regions within the brain tissue yield similar statistical properties at different scales of magnification. Fractal-based analysis of the histological features of brain tumors can reveal the underlying complexity of tissue structure and angiostructure, also providing an indication of tissue abnormality development. It can further be used to quantify the chaotic signature of disease in order to distinguish between different temporal tumor stages and histopathological grades.

Brain meningioma subtype classifications improvement from histopathological images is the main focus of this chapter. Meningioma tissue texture exhibits a wide range of histological patterns whereby a single slide may show a combination of multiple patterns. Distinctive fractal patterns quantified in a multiresolution manner would be for better spatial relationship representation. Fractal features extracted from textural tissue patterns can be useful in characterizing meningioma tumors in terms of subtype classification, a challenging problem compared to histological grading, and furthermore can provide an objective measure for quantifying subtle features within subtypes that are hard to discriminate.

Keywords

Fractal dimension Texture analysis Brain histopathology Meningioma Tissue characterization Pattern classification 

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Institute of BioengineeringEcole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
  2. 2.King Abdullah II School for ITUniversity of JordanAmmanJordan
  3. 3.Neurosurgery Unit, Faculty of Medicine and Health SciencesMacquarie UniversitySydneyAustralia
  4. 4.Garvan Institute of Medical ResearchSydneyAustralia
  5. 5.Medical University of ViennaViennaAustria
  6. 6.University of TorontoTorontoCanada

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