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RESEARCH PRODUCT

Probabilistic classification of intracranial gliomas in digital microscope images based on EGFR quantity

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description

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.