Abstract
| Language | English |
|---|---|
| Title of host publication | The Fractal geometry of the brain |
| Editors | Antonio Di Ieva |
| Place of Publication | New York |
| Publisher | Springer, Springer Nature |
| Pages | 371-391 |
| Number of pages | 21 |
| ISBN (Print) | 9781493939954 |
| DOIs | |
| Publication status | Published - 2016 |
Publication series
| Name | Springer Series in Computational Neuroscience |
|---|---|
| Publisher | Springer |
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Keywords
- fractal dimension
- texture analysis
- brain histopathology
- meningioma
- tissue characterization
- pattern classification
Cite this
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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 proceeding › Chapter › Research › peer-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 -