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Proceedings Paper

Probabilistic classification of intracranial gliomas in digital microscope images based on EGFR quantity
Author(s): Marcin Grzegorzek; Marianna Buckan; Sigrid Horn
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Paper Abstract

A glioma is a type of cancer occurring, in the majority of cases, in the brain. The World Health Organization (WHO) assigns a grade from I to IV to this tumor, with I being the least aggressive and IV being the most aggressive. In glioma cells of grade IV the Epidermal Growth Factor Receptors (EGFRs) are over expressed. In this paper we hypothesize that this overexpression occurs also for gliomas of grades I to III. Moreover, we present a medical study aiming to determine the correlation between the WHO classification and the EGFR quantity in glioma tissue. We define five quantity classes for EGFR. First, results of immunohistochemical staining on brain glioma slices, which visualize the EGFR quantity, are examined under an optical microscope and manually classified into these five classes. In this paper we propose to perform this classification automatically using statistical pattern recognition technique for digital images. For this, digital microscope images of glioma are acquired and their histograms computed. Afterwards, all five EGFR quantity classes (image classes) are statistically modeled using training samples. This allows a fully automatic classification of unknown images into one of the five classes using the Maximum Likelihood (ML) estimation. Experimental results show that, on the one hand, the automatic EGFR quantity classification performs with a quite high accuracy, on the other hand, it is done much faster than manual labeling done by a human.

Paper Details

Date Published: 27 March 2009
PDF: 8 pages
Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72591T (27 March 2009); doi: 10.1117/12.811552
Show Author Affiliations
Marcin Grzegorzek, Univ. of Koblenz-Landau (Germany)
Marianna Buckan, Johannes Gutenberg Univ. of Mainz (Germany)
Sigrid Horn, Johannes Gutenberg Univ. of Mainz (Germany)

Published in SPIE Proceedings Vol. 7259:
Medical Imaging 2009: Image Processing
Josien P. W. Pluim; Benoit M. Dawant, Editor(s)

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