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.
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