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Cluster headache diagnosis using iris color features and statistical pixel classification

Abstract:

Cluster headache diagnosis is an open problem, whose signs are often noticeable in terms of changes in texture, color, or clarity of the eye of the induced pain side. The color difference is sometimes visible by the naked eye, hence it is close to the diagnosis of the cluster headache. Preliminary diagnosis tests on patients with cluster headache have shown that all of them have hypo-pigmented iris in the symptomatic side. We present here a color quantization method which can document the pigmentation differences of the iris, aiming to develop the first step towards an automatic diagnosis detection system. To do this, we used a statistical learning approach, specifically a Support Vector Classifier, to detect differences between iris pigmentation by using the error probability as a surrogate of color differences between the pigmentation in both eyes. The classification performance was compared when considering several features from different color spaces, in such a way that the error probability provided by the classifier when comparing the iris color of both eyes of the same patient provides a quantitative measure of the headache diagnosis. Systematic tests were performed on a database with images of 11 patients (seven patients with cluster headache and four control subjects). We can conclude from the present work that the study of the iris color features through statistical learning emerges as a technique of interest in the study of disorders affecting the sympathetic system.