Histological fractal-based classification of brain tumors

Omar S. Al-Kadi, Antonio Di Ieva

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

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.
LanguageEnglish
Title of host publicationThe Fractal geometry of the brain
EditorsAntonio Di Ieva
Place of PublicationNew York
PublisherSpringer, Springer Nature
Pages371-391
Number of pages21
ISBN (Print)9781493939954
DOIs
Publication statusPublished - 2016

Publication series

NameSpringer Series in Computational Neuroscience
PublisherSpringer

Fingerprint

Fractals
Brain Neoplasms
Meningioma
Brain
Neoplasms

Keywords

  • fractal dimension
  • texture analysis
  • brain histopathology
  • meningioma
  • tissue characterization
  • pattern classification

Cite this

Al-Kadi, O. S., & Di Ieva, A. (2016). Histological fractal-based classification of brain tumors. In A. Di Ieva (Ed.), The Fractal geometry of the brain (pp. 371-391). (Springer Series in Computational Neuroscience). New York: Springer, Springer Nature. https://doi.org/10.1007/978-1-4939-3995-4_23
Al-Kadi, Omar S. ; Di Ieva, Antonio. / Histological fractal-based classification of brain tumors. The Fractal geometry of the brain. editor / Antonio Di Ieva. New York : Springer, Springer Nature, 2016. pp. 371-391 (Springer Series in Computational Neuroscience).
@inbook{d59180e139ef44938476ce5e800d2e87,
title = "Histological fractal-based classification of brain tumors",
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",
author = "Al-Kadi, {Omar S.} and {Di Ieva}, Antonio",
year = "2016",
doi = "10.1007/978-1-4939-3995-4_23",
language = "English",
isbn = "9781493939954",
series = "Springer Series in Computational Neuroscience",
publisher = "Springer, Springer Nature",
pages = "371--391",
editor = "{Di Ieva}, Antonio",
booktitle = "The Fractal geometry of the brain",
address = "United States",

}

Al-Kadi, OS & Di Ieva, A 2016, Histological fractal-based classification of brain tumors. in A Di Ieva (ed.), The Fractal geometry of the brain. Springer Series in Computational Neuroscience, Springer, Springer Nature, New York, pp. 371-391. https://doi.org/10.1007/978-1-4939-3995-4_23

Histological fractal-based classification of brain tumors. / Al-Kadi, Omar S.; Di Ieva, Antonio.

The Fractal geometry of the brain. ed. / Antonio Di Ieva. New York : Springer, Springer Nature, 2016. p. 371-391 (Springer Series in Computational Neuroscience).

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

TY - CHAP

T1 - Histological fractal-based classification of brain tumors

AU - Al-Kadi, Omar S.

AU - Di Ieva, Antonio

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - fractal dimension

KW - texture analysis

KW - brain histopathology

KW - meningioma

KW - tissue characterization

KW - pattern classification

U2 - 10.1007/978-1-4939-3995-4_23

DO - 10.1007/978-1-4939-3995-4_23

M3 - Chapter

SN - 9781493939954

T3 - Springer Series in Computational Neuroscience

SP - 371

EP - 391

BT - The Fractal geometry of the brain

A2 - Di Ieva, Antonio

PB - Springer, Springer Nature

CY - New York

ER -

Al-Kadi OS, Di Ieva A. Histological fractal-based classification of brain tumors. In Di Ieva A, editor, The Fractal geometry of the brain. New York: Springer, Springer Nature. 2016. p. 371-391. (Springer Series in Computational Neuroscience). https://doi.org/10.1007/978-1-4939-3995-4_23