Today, dry eye disease is a widely seen health problem. It is known that the disease affects %25-%30 of society. In the absence of early diagnosis and treatment, it may cause the occurrence of significant ocular surface damage and then formation of severe eye discomforts. Because of this, at the point of diagnosis and treatment of the common disease an automatic system that provides faster, more accurate and objective decision-making process rather than clinical care is needed to facilitate the work of the experts. Accurate determination of region of interest has high priority for the algorithm to be applied in the diagnosis of dry eye disease. In this study, automatic detection and extraction of region of interest is studied on real dry eye patient data received after applying clinical fluorescein staining test.